TY - GEN A1 - Kaletta, Udo Christian A1 - Wipf, Christian A1 - Fraschke, Mirko A1 - Wolansky, Dirk A1 - Schubert, Markus Andreas A1 - Schroeder, Thomas A1 - Wenger, Christian T1 - AlN/SiO2/Si3N4/Si(100) based CMOS compatible surface acoustic wave filter with -12.8 dB minimum insertion loss T2 - IEEE Transactions on Electron Devices Y1 - 2015 U6 - https://doi.org/10.1109/TED.2015.2395443 SN - 0018-9383 VL - 62 IS - 3 SP - 764 EP - 768 ER - TY - GEN A1 - Bertaud, Thomas A1 - Walczyk, Damian A1 - Walczyk, Christian A1 - Kubotsch, S. A1 - Wenger, Christian A1 - Schröder, Thomas A1 - Vallée, Christophe A1 - Gonon, P. A1 - Mannequin, C. A1 - Jousseaume, V. A1 - Grampeix, Helen T1 - Resistive Switching of HfO2-based MIM diodes: Impact of the Top Electrode Materials T2 - Thin Solid Films Y1 - 2012 SN - 0040-6090 VL - 520 IS - 14 SP - 4551 EP - 4555 ER - TY - GEN A1 - Bertaud, Thomas A1 - Walczyk, Damian A1 - Sowinska, Małgorzata A1 - Wolansky, Dirk A1 - Tillack, Bernd A1 - Schoof, Gunther A1 - Korolevych, R. A1 - Wenger, Christian A1 - Thiess, Sebastian A1 - Schroeder, Thomas A1 - Walczyk, Christian T1 - HfO2-based RRAM for Embedded Nonvolatile Memory: From Materials Science to Integrated 1T1R RRAM Arrays T2 - ECS transactions Y1 - 2012 SN - 1938-6737 VL - 50 IS - 4 SP - 21 EP - 26 ER - TY - GEN A1 - Pérez, Eduardo A1 - Maldonado, David A1 - Acal, Christian A1 - Ruiz-Castro, Juan Eloy A1 - Aguilera, Ana María A1 - Jimenez-Molinos, Francisco A1 - Roldan, Juan Bautista A1 - Wenger, Christian T1 - Advanced Temperature Dependent Statistical Analysis of Forming Voltage Distributions for Three Different HfO2-Based RRAM Technologies T2 - Solid State Electronics N2 - In this work, voltage distributions of forming operations are analyzed by using an advanced statistical approach based on phase-type distributions (PHD). The experimental data were collected from batches of 128 HfO2-based RRAM devices integrated in 4-kbit arrays. Three di erent switching oxides, namely, polycrystalline HfO2, amorphous HfO2, and Al-doped HfO2, were tested in the temperature range from -40 to 150 oC. The variability of forming voltages has been usually studied by using the Weibull distribution (WD). However, the performance of the PHD analysis demonstrated its ability to better model this crucial operation. The capacity of the PHD to reproduce the experimental data has been validated by means of the Kolmogorov-Smirnov test, while the WD failed in many of the cases studied. In addition, PHD allows to extract information about intermediate probabilistic states that occur in the forming process and the transition probabilities between them; in this manner, we can deepen on the conductive lament formation physics. In particular, the number of intermediate states can be related to the device variability. KW - RRAM KW - HfO2 Y1 - 2021 SN - 0038-1101 SN - 1879-2405 VL - 176 ER - TY - GEN A1 - Mai, Christian A1 - Marschmeyer, Steffen A1 - Peczek, Anna A1 - Kroh, Aleksandra A1 - Jose, Josmy A1 - Reiter, Sebastian A1 - Fischer, Inga Anita A1 - Wenger, Christian A1 - Mai, Andreas T1 - Integration Aspects of Plasmonic TiN-based Nano-Hole-Arrays on Ge Photodetectorsin a 200mm Wafer CMOS Compatible Silicon Technology T2 - ECS Transactions N2 - In this work we present the progress in regard to the integration of a surface plasmon resonance refractive index sensor into a CMOS compatible 200 mm wafer silicon-based technology. Our approach pursues the combination of germanium photodetectors with metallic nanohole arrays. The paper is focused on the technology development to fabricate large area photodetectors based on a modern design concept. In a first iteration we achieved a leakage current density of 82 mA/cm2 at reverse bias of 0.5 V and a maximum optical responsivity of 0.103 A/W measured with TE polarized light at λ = 1310 nm and a reversed bias of 1 V. For the realization of nanohole arrays we used thin Titanium nitride (TiN) layers deposited by a sputtering process. We were able to produce very homogenous TiN layers with a thickness deviation of around 10 % and RMS of 1.413 nm for 150 nm thick TiN layers. KW - plasmonics KW - nanohole array KW - germanium detector Y1 - 2022 U6 - https://doi.org/10.1149/10904.0035ecst SN - 1938-5862 VL - 109 IS - 4 SP - 35 EP - 46 ER - TY - GEN A1 - Reiter, Sebastian A1 - Sengül, Akant A1 - Mai, Christian A1 - Spirito, Davide A1 - Wenger, Christian A1 - Fischer, Inga Anita T1 - On-chip refractive index sensors based on plasmonic TiN Nanohole Arrays T2 - 2024 IEEE Silicon Photonics Conference (SiPhotonics) Y1 - 2024 SN - 979-8-3503-9404-7 SN - 979-8-3503-9405-4 U6 - https://doi.org/10.1109/SiPhotonics60897.2024.10544048 SN - 1949-209X ER - TY - GEN A1 - Jose, Josmy A1 - Mai, Christian A1 - Wenger, Christian A1 - Fischer, Inga Anita T1 - Integration concept of plasmonic TiN nanohole arrays in a 200 mm BiCMOS Si technology for refractive index sensor applications T2 - iCCC2024 - iCampµs Cottbus Conference Y1 - 2024 U6 - https://doi.org/10.5162/iCCC2024/7.2 SP - 96 EP - 99 ER - TY - GEN A1 - Mai, Christian A1 - Peczek, Anna A1 - Kroh, Aleksandra A1 - Jose, Josmy A1 - Reiter, Sebastian A1 - Wenger, Christian A1 - Fischer, Inga Anita T1 - Towards a CMOS compatible refractive index sensor: cointegration of TiN nanohole arrays and Ge photodetectors in a 200 mm wafer silicon technology T2 - Optics Express N2 - In this work, we present the monolithic integration of a TiN nanohole array and a Ge photodetector towards a CMOS compatible fabrication of a refractive index sensor in a 200 mm wafer silicon technology. We developed a technology process that enables fabrication with high yields of around 90%. Ge photodetectors with a Ge layer thickness of 450 nm and an area of 1600 µm2 (40 µm x 40 µm) show dark current densities of around 129 mA/cm2 and responsivities of 0.114 A/W measured by top illumination (TE polarization; λ = 1310 nm; angle of incidence = 14 °) at a reverse bias of 1 V. Nanohole arrays were structured in a 150 nm thick TiN layer. They were integrated into the back end of line and placed spatially close to the Ge photodetectors. After the metallization, passivation, and pad opening, the nanohole arrays were released with the help of an amorphous silicon stop layer. A significant impact of the TiN nanohole arrays on the optical behavior of the photodetector could be proven on the wafer level. Photocurrent measurements by top illumination confirm a strong dependence of optical properties on the polarization of the incident light and the nanohole array design. We demonstrate very stable photocurrents on the wafer level with a standard deviation of σ < 6%. KW - plasmonics KW - sensor KW - nano hole array Y1 - 2024 U6 - https://doi.org/10.1364/OE.530081 SN - 1094-4087 VL - 32 IS - 17 SP - 29099 EP - 29111 PB - Optica Publishing Group ER - TY - GEN A1 - Dersch, Nadine A1 - Roemer, Christian A1 - Perez, Eduardo A1 - Wenger, Christian A1 - Schwarz, Mike A1 - Iñíguez, Benjamín A1 - Kloes, Alexander T1 - Fast circuit simulation of memristive crossbar arrays with bimodal stochastic synaptic weights T2 - 2024 IEEE Latin American Electron Devices Conference (LAEDC) N2 - This paper presents an approach for highly efficient circuit simulation of hardware-based artificial neural networks by using memristive crossbar array architectures. There are already possibilities to test neural networks with stochastic weights via simulations like the macro model NeuroSim. However, the noise-based variability approach offers more realistic setting options including elements of a classical circuit simulation for more precise analysis of neural networks. With this approach, statistical parameter fluctuations can be simulated based on different distribution functions of devices. In Cadence Virtuoso, a simulation of a crossbar array with 10 synaptic weights following a bimodal distribution, the new approach shows a 1,000x speedup compared to a Monte Carlo simulation. Initial tests of a memristive crossbar array with over 15,000 stochastic weights to classify the MNIST dataset show that the new approach can be used to test the functionality of hardware-based neural networks. KW - RRAM Y1 - 2024 SN - 979-8-3503-6130-8 U6 - https://doi.org/10.1109/LAEDC61552.2024.10555829 SN - 979-8-3503-6129-2 SN - 2835-3471 SP - 1 EP - 4 PB - IEEE ER - TY - GEN A1 - Reiter, Sebastian A1 - Han, Weijia A1 - Mai, Christian A1 - Spirito, Davide A1 - Jose, Josmy A1 - Zöllner, Marvin Hartwig A1 - Fursenko, Oksana A1 - Schubert, Markus Andreas A1 - Stemmler, Ivo A1 - Wenger, Christian A1 - Fischer, Inga Anita T1 - Titanium Nitride Plasmonic Nanohole Arrays for CMOS-compatible integrated refractive index sensing: influence of layer thickness on optical properties T2 - Plasmonics Y1 - 2023 U6 - https://doi.org/10.1007/s11468-023-01810-3 SN - 1557-1963 SP - 1 EP - 13 ER - TY - GEN A1 - Han, Weijia A1 - Reiter, Sebastian A1 - Schlipf, Jon A1 - Mai, Christian A1 - Spirito, Davide A1 - Jose, Josmy A1 - Wenger, Christian A1 - Fischer, Inga Anita T1 - Strongly enhanced sensitivities of CMOS compatible plasmonic titanium nitride nanohole arrays for refractive index sensing under oblique incidence T2 - Optics Express N2 - Titanium nitride (TiN) is a complementary metal-oxide-semiconductor (CMOS) compatible material with large potential for the fabrication of plasmonic structures suited for device integration. However, the comparatively large optical losses can be detrimental for application. This work reports a CMOS compatible TiN nanohole array (NHA) on top of a multilayer stack for potential use in integrated refractive index sensing with high sensitivities at wavelengths between 800 and 1500 nm. The stack, consisting of the TiN NHA on a silicon dioxide (SiO2) layer with Si as substrate (TiN NHA/SiO2/Si), is prepared using an industrial CMOS compatible process. The TiN NHA/SiO2/Si shows Fano resonances in reflectance spectra under oblique excitation, which are well reproduced by simulation using both finite difference time domain (FDTD) and rigorous coupled-wave analysis (RCWA) methods. The sensitivities derived from spectroscopic characterizations increase with the increasing incident angle and match well with the simulated sensitivities. Our systematic simulation-based investigation of the sensitivity of the TiN NHA/SiO2/Si stack under varied conditions reveals that very large sensitivities up to 2305 nm per refractive index unit (nm RIU−1) are predicted when the refractive index of superstrate is similar to that of the SiO2 layer. We analyze in detail how the interplay between plasmonic and photonic resonances such as surface plasmon polaritons (SPPs), localized surface plasmon resonances (LSPRs), Rayleigh Anomalies (RAs), and photonic microcavity modes (Fabry-Pérot resonances) contributes to this result. This work not only reveals the tunability of TiN nanostructures for plasmonic applications but also paves the way to explore efficient devices for sensing in broad conditions. KW - TiN KW - Plasmonics Y1 - 2023 U6 - https://doi.org/10.1364/OE.481993 SN - 1094-4087 VL - 31 IS - 11 SP - 17389 EP - 17407 ER - TY - GEN A1 - Dersch, Nadine A1 - Perez-Bosch Quesada, Emilio A1 - Pérez, Eduardo A1 - Wenger, Christian A1 - Roemer, Christian A1 - Schwarz, Mike A1 - Kloes, Alexander T1 - Efficient circuit simulation of a memristive crossbar array with synaptic weight variability T2 - Solid State Electronics N2 - In this paper, we present a method for highly-efficient circuit simulation of a hardware-based artificial neural network realized in a memristive crossbar array. The statistical variability of the devices is considered by a noise-based simulation technique. For the simulation of a crossbar array with 8 synaptic weights in Cadence Virtuoso the new approach shows a more than 200x speed improvement compared to a Monte Carlo approach, yielding the same results. In addition, first results of an ANN with more than 15,000 memristive devices classifying test data of the MNIST dataset are shown, for which the speed improvement is expected to be several orders of magnitude. Furthermore, the influence on the classification of parasitic resistances of the connection lines in the crossbar is shown. KW - RRAM KW - Neural network Y1 - 2023 U6 - https://doi.org/10.1016/j.sse.2023.108760 SN - 0038-1101 VL - 209 ER - TY - GEN A1 - Reiter, Sebastian A1 - Ratzke, Markus A1 - Nitsch, Paul-Gregor A1 - Mai, Christian A1 - Spirito, Davide A1 - Corley-Wiciak, Agnieszka Anna A1 - Wenger, Christian A1 - Fischer, Inga A. T1 - Optical response of titanium nitride plasmonic nanohole arrays : impact of square and hexagonal array geometry, pitch, and nanohole diameter T2 - Plasmonics N2 - Plasmonic nanohole arrays (NHAs) exhibit extraordinary optical transmission (EOT) evoked by resonant excitation of surface plasmons at an excitation wavelength, which is highly sensitive to changes in refractive index in the surrounding dielectric. This can enable the use of plasmonic NHAs in on-chip refractive index sensors. Such sensors can be realized on the cost-effective silicon platform by the integration of a plasmonic NHA and a Ge photodetector, provided that complementary metal–oxide–semiconductor (CMOS)-compatible materials are used for their fabrication. Titanium nitride (TiN) as a biocompatible and CMOS-compatible plasmonic transition metal nitride is well-suited for integration on the silicon platform; however, the comparatively large losses within the material require geometry optimization strategies in order to improve the optical properties of TiN NHAs for sensing. In this work, we investigated different TiN NHA geometries both in experiment and simulation. We extensively characterized square and hexagonal arrays with varying pitches and nanohole diameters and provide a detailed comparison of their optical properties. We also discuss characterization results for surface refractive index changes imposed by depositing a thin Al2O3 layer on top of the NHAs. While we do not observe a clear advantage of hexagonal arrays compared to square arrays for sensing, our results highlight the importance of geometry optimization for TiN NHAs integrated with devices. KW - Plasmonic nanohole array KW - Titanium nitride KW - Hexagonal array KW - Square array KW - Nanohole diameters Y1 - 2025 UR - https://link.springer.com/article/10.1007/s11468-025-02934-4 U6 - https://doi.org/10.1007/s11468-025-02934-4 VL - 20 SP - 8825 EP - 8834 PB - Springer CY - New York, NY ER - TY - GEN A1 - Lupina, Grzegorz A1 - Dabrowski, Jarek Marek A1 - Formanek, Peter A1 - Schmeißer, Dieter A1 - Sorge, Roland A1 - Wenger, Christian A1 - Zaumseil, Peter A1 - Müssig, Hans-Joachim T1 - Solid-state reaction between Pr and SiO2 studied by photoelectron spectroscopy and ab initio calculations T2 - Materials Science in Semiconductor Processing N2 - We report on the structural and electrical properties of Pr-based high-k dielectric films fabricated by solid-state reaction between metallic Pr and SiO2 underlayers. A non-destructive depth profiling using synchrotron radiation excited photoelectron spectroscopy (SR-PES), X-ray photoelectron spectroscopy (XPS) and transmission electron microscopy (TEM) were employed to examine the chemical composition and microstructure. Ab initio calculations were done to gain insight into the physical processes involved. SR-PES results indicate that Pr deposition at room temperature (RT) leads to the formation of a Pr silicide and a Pr oxide, what is in good agreement with the scenario expected from ab initio calculations. As revealed by TEM and electrical measurements, oxidation of the reacted structures, followed by annealing, results in a stacked dielectric composed of a SiO2-based buffer with an enhanced permittivity and a Pr silicate film with a high dielectric constant. The leakage current density of 10-4 A/cm2 was measured for stacks with capacitance equivalent thickness (CET) of 1.5 nm prepared by evaporation of the Pr layer on a 1.8 nm SiO2 film, followed by oxidation in air ambient and annealing in N2 atmosphere. The capacitance-voltage (C-V) curves exhibit a large flatband voltage (VFB) shift indicating the presence of a positive charge in the stack. Switching away from the Al contacts to Au gate electrodes introduces a significant reduction of the VFB by 1.3 eV, what is much more than the change expected from the work function difference between Al and Au (not, vert, similar0.9 eV). This in turn implies that VFB is strongly affected by the gate interface electrode. KW - High-k dielectrics KW - Photoelectron spectroscopy KW - Interface reaction Y1 - 2004 SN - 1369-8001 VL - 7 IS - 4-6 SP - 215 EP - 220 ER - TY - GEN A1 - Müssig, Hans-Joachim A1 - Dabrowski, Jarek Marek A1 - Wenger, Christian A1 - Lupina, Grzegorz A1 - Sorge, Roland A1 - Formanek, Peter A1 - Zaumseil, Peter A1 - Schmeißer, Dieter T1 - Ultrathin Dielectric Films Grown by Solid Phase Reaction of Pr with SiO2 T2 - MRS Proceedings Y1 - 2004 SN - 1946-4274 VL - 811 ER - TY - JOUR A1 - Sohal, Rakesh A1 - Lupina, Grzegorz A1 - Lippert, Gunther A1 - Wenger, Christian A1 - Seifarth, Olaf A1 - Schröder, Thomas A1 - Tallarida, Massimo A1 - Schmeißer, Dieter T1 - Interface chemistry of high-k PrxAl2-xO3 (x=2-0) dielectrics on TiN for dynamic random access memory applications KW - high-k KW - dielectric KW - random Y1 - 2008 ER - TY - GEN A1 - Dirkmann, Sven A1 - Kaiser, Jan A1 - Wenger, Christian A1 - Mussenbrock, Thomas T1 - Filament Growth and Resistive Switching in Hafnium Oxide Memristive Devices T2 - Plasma Sources Science and Technology Y1 - 2018 U6 - https://doi.org/10.1021/acsami.7b19836 SN - 1361-6595 SN - 0963-0252 VL - 10 IS - 17 SP - 14857 EP - 14868 ER - TY - GEN A1 - Romero-Zaliz, Rocío A1 - Pérez, Eduardo A1 - Jimenez-Molinos, Francisco A1 - Wenger, Christian A1 - Roldan, Juan Bautista T1 - Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches T2 - Electronics (MDPI) N2 - A comprehensive analysis of two types of artificial neural networks (ANN) is performed to assess the influence of quantization on the synaptic weights. Conventional multilayer-perceptron (MLP) and convolutional neural networks (CNN) have been considered by changing their features in the training and inference contexts, such as number of levels in the quantization process, the number of hidden layers on the network topology, the number of neurons per hidden layer, the image databases, the number of convolutional layers, etc. A reference technology based on 1T1R structures with bipolar memristors including HfO2 dielectrics was employed, accounting for different multilevel schemes and the corresponding conductance quantization algorithms. The accuracy of the image recognition processes was studied in depth. This type of studies are essential prior to hardware implementation of neural networks. The obtained results support the use of CNNs for image domains. This is linked to the role played by convolutional layers at extracting image features and reducing the data complexity. In this case, the number of synaptic weights can be reduced in comparison to conventional MLPs. KW - RRAM KW - resistive switching KW - neural network Y1 - 2021 U6 - https://doi.org/10.3390/electronics10030346 SN - 2079-9292 VL - 10 IS - 3 ER - TY - GEN A1 - Soltani Zarrin, Pouya A1 - Zahari, Finn A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Pérez, Eduardo A1 - Kohlstedt, Hermann A1 - Wenger, Christian T1 - Neuromorphic on‑chip recognition of saliva samples of COPD and healthy controls using memristive devices T2 - Scientific Reports N2 - Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. The results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD. KW - RRAM KW - memristive device KW - neural network Y1 - 2020 U6 - https://doi.org/10.1038/s41598-020-76823-7 SN - 2045-2322 VL - 10 ER - TY - GEN A1 - Soltani Zarrin, Pouya A1 - Rockendorf, Niels A1 - Wenger, Christian T1 - In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools T2 - IEEE Access N2 - Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease and a major cause of morbidity and mortality worldwide. Although a curative therapy has yet to be found, permanent monitoring of biomarkers that reflect the disease progression plays a pivotal role for the effective management of COPD. The accurate examination of respiratory tract fluids like saliva is a promising approach for staging the disease and predicting its upcoming exacerbations in a Point-of-Care (PoC) environment. Nonetheless, this approach is only feasible by concurrent consideration of patients' demographic and medical parameters. Therefore, Machine Learning (ML) tools are necessary for the comprehensive recognition of COPD in a PoC setting. As a result, the objective of this work was to implement ML tools on the data acquired from characterizing saliva samples of COPD patients and healthy controls for classification purposes. First, a permittivity biosensor was used to characterize dielectric properties of saliva samples and, subsequently, ML tools were applied on the acquired data for classification. The XGBoost gradient boosting algorithm provided a high classification accuracy of 91.25%, making it a promising model for COPD recognition. Integration of this model on a neuromorphic chip, in the future, will enable the real-time detection of COPD in PoC, with low energy consumption and high patient privacy. KW - COPD KW - Machine learning KW - Point of care Y1 - 2020 U6 - https://doi.org/10.1109/ACCESS.2020.3023971 SN - 2169-3536 VL - Vol. 8 SP - 168053 EP - 168060 ER - TY - GEN A1 - Soltani Zarrin, Pouya A1 - Wenger, Christian T1 - Implementation of Siamese-Based Few-Shot Learning Algorithms for the Distinction of COPD and Asthma Subjects T2 - Artificial Neural Networks and Machine Learning – ICANN 2020 : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I N2 - This paper investigates the practicality of applying braininspired Few-Shot Learning (FSL) algorithms for addressing shortcomings of Machine Learning (ML) methods in medicine with limited data availability. As a proof of concept, the application of ML for the detection of Chronic Obstructive Pulmonary Disease (COPD) patients was investigated. The complexities associated with the distinction of COPD and asthma patients and the lack of sufficient training data for asthma subjects impair the performance of conventional ML models for the recognition of COPD. Therefore, the objective of this study was to implement FSL methods for the distinction of COPD and asthma subjects with a few available data points. The proposed FSL models in this work were capable of recognizing asthma and COPD patients with 100% accuracy, demonstrating the feasibility of the approach for applications such as medicine with insufficient data availability. KW - Machine Learning KW - COPD Y1 - 2020 U6 - https://doi.org/10.1007/978-3-030-61609-0_34 SP - 431 EP - 440 PB - Springer CY - Cham ER - TY - GEN A1 - Zanotti, Tommaso A1 - Puglisi, Francesco Maria A1 - Milo, Valerio A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Ossorio, Óscar G. A1 - Wenger, Christian A1 - Pavan, Paolo A1 - Olivo, Piero A1 - Ielmini, Daniele T1 - Reliability of Logic-in-Memory Circuits in Resistive Memory Arrays T2 - IEEE Transactions on Electron Devices N2 - Logic-in-memory (LiM) circuits based on resistive random access memory (RRAM) devices and the material implication logic are promising candidates for the development of low-power computing devices that could fulfill the growing demand of distributed computing systems. However, these circuits are affected by many reliability challenges that arise from device nonidealities (e.g., variability) and the characteristics of the employed circuit architecture. Thus, an accurate investigation of the variability at the array level is needed to evaluate the reliability and performance of such circuit architectures. In this work, we explore the reliability and performance of smart IMPLY (SIMPLY) (i.e., a recently proposed LiM architecture with improved reliability and performance) on two 4-kb RRAM arrays based on different resistive switching oxides integrated in the back end of line (BEOL) of the 0.25- μm BiCMOS process. We analyze the tradeoff between reliability and energy consumption of SIMPLY architecture by exploiting the results of an extensive array-level variability characterization of the two technologies. Finally, we study the worst case performance of a full adder implemented with the SIMPLY architecture and benchmark it on the analogous CMOS implementation. KW - RRAM KW - in-memory computing KW - HfO2 Y1 - 2020 U6 - https://doi.org/10.1109/TED.2020.3025271 SN - 0018-9383 SN - 1557-9646 VL - 67 IS - 11 SP - 4611 EP - 4615 ER - TY - GEN A1 - Zahari, Finn A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Kohlstedt, Hermann A1 - Wenger, Christian A1 - Ziegler, Martin T1 - Analogue pattern recognition with stochastic switching binary CMOS‑integrated memristive devices T2 - Scientific Reports N2 - Biological neural networks outperform todays computer technology in terms of power consumption and computing speed when associative tasks, like pattern recognition, are to be solved. The analogue and massive parallel in-memory computing in biology differs strongly with conventional transistor electronics using the von Neumann architecture. Therefore, novel bio-inspired computing architectures are recently highly investigated in the area of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are used to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology in neuromorphic systems. However, dealing with the inherent stochasticity of the resistive switching effect can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail and their use in a stochastic artificial neural network (StochANN) to solve the MINST pattern recognition task is examined. A mixed-signal implementation with hardware synapses and software neurons as well as numerical simulations show the proposed concept of stochastic computing is able to handle analogue data with binary memory cells. KW - RRAM KW - memristive device KW - neural network KW - HfO2 Y1 - 2020 U6 - https://doi.org/10.1038/s41598-020-71334-x SN - 2045-2322 VL - 10 ER - TY - GEN A1 - Petzold, Stefan A1 - Piros, Eszter A1 - Eilhardt, Robert A1 - Zintler, Alexander A1 - Vogel, Tobias A1 - Kaiser, Nico A1 - Radetinac, Aldin A1 - Komissinskiy, Philipp A1 - Jalaguier, Eric A1 - Nolot, Emmanuel A1 - Charpin-Nicolle, Christelle A1 - Wenger, Christian A1 - Molina-Luna, Leopoldo A1 - Miranda, Enrique A1 - Alff, Lambert T1 - Tailoring the Switching Dynamics in Yttrium Oxide-Based RRAM Devices by Oxygen Engineering: From Digital to Multi-Level Quantization toward Analog Switching T2 - Advanced Electronic Materials N2 - This work investigates the transition from digital to gradual or analog resistive switching in yttrium oxide‐based resistive random‐access memory devices. It is shown that this transition is determined by the amount of oxygen in the functional layer. A homogeneous reduction of the oxygen content not only reduces the electroforming voltage, allowing for forming‐free devices, but also decreases the voltage operation window of switching, thereby reducing intra‐device variability. The most important effect as the dielectric becomes substoichiometric by oxygen engineering is that more intermediate (quantized) conduction states are accessible. A key factor for this reproducibly controllable behavior is the reduced local heat dissipation in the filament region due to the increased thermal conductivity of the oxygen depleted layer. The improved accessibility of quantized resistance states results in a semi‐gradual switching both for the set and reset processes, as strongly desired for multi‐bit storage and for an accurate definition of the synaptic weights in neuromorphic systems. A theoretical model based on the physics of mesoscopic structures describing current transport through a nano‐constriction including asymmetric potential drops at the electrodes and non‐linear conductance quantization is provided. The results contribute to a deeper understanding on how to tailor materials properties for novel memristive functionalities. KW - RRAM KW - Multilevel switching Y1 - 2020 U6 - https://doi.org/10.1002/aelm.202000439 SN - 2199-160X VL - 6 IS - 11 ER - TY - GEN A1 - Perez-Avila, Antonio Javier A1 - Gonzalez-Cordero, Gerardo A1 - Pérez, Eduardo A1 - Perez-Bosch Quesada, Emilio A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Wenger, Christian A1 - Roldan, Juan Bautista A1 - Jimenez-Molinos, Francisco T1 - Behavioral modeling of multilevel HfO2-based memristors for neuromorphic circuit simulation T2 - XXXV Conference on Design of Circuits and Integrated Systems (DCIS), Segovia, Spain N2 - An artificial neural network based on resistive switching memristors is implemented and simulated in LTspice. The influence of memristor variability and the reduction of the continuous range of synaptic weights into a discrete set of conductance levels is analyzed. To do so, a behavioral model is proposed for multilevel resistive switching memristors based on Al-doped HfO2 dielectrics, and it is implemented in a spice based circuit simulator. The model provides an accurate description of the conductance in the different conductive states in addition to describe the device-to-device variability KW - RRAM KW - Multilevel switching KW - behavorial model Y1 - 2020 U6 - https://doi.org/10.1109/DCIS51330.2020.9268652 ER - TY - GEN A1 - Pérez, Eduardo A1 - Ossorio, Óscar G. A1 - Dueñas, Salvador A1 - Castán, Helena A1 - García, Hector A1 - Wenger, Christian T1 - Programming Pulse Width Assessment for Reliable and Low-Energy Endurance Performance in Al:HfO2-Based RRAM Arrays T2 - Electronics (MDPI) N2 - A crucial step in order to achieve fast and low-energy switching operations in resistive random access memory (RRAM) memories is the reduction of the programming pulse width. In this study, the incremental step pulse with verify algorithm (ISPVA) was implemented by using different pulse widths between 10 μ s and 50 ns and assessed on Al-doped HfO 2 4 kbit RRAM memory arrays. The switching stability was assessed by means of an endurance test of 1k cycles. Both conductive levels and voltages needed for switching showed a remarkable good behavior along 1k reset/set cycles regardless the programming pulse width implemented. Nevertheless, the distributions of voltages as well as the amount of energy required to carry out the switching operations were definitely affected by the value of the pulse width. In addition, the data retention was evaluated after the endurance analysis by annealing the RRAM devices at 150 °C along 100 h. Just an almost negligible increase on the rate of degradation of about 1 μ A at the end of the 100 h of annealing was reported between those samples programmed by employing a pulse width of 10 μ s and those employing 50 ns. Finally, an endurance performance of 200k cycles without any degradation was achieved on 128 RRAM devices by using programming pulses of 100 ns width KW - RRAM KW - Reliability Y1 - 2020 U6 - https://doi.org/10.3390/electronics9050864 SN - 2079-9292 VL - 9 IS - 5 ER - TY - GEN A1 - Morales, Carlos A1 - Mahmoodinezhad, Ali A1 - Schubert, Andreas Markus A1 - Wenger, Christian A1 - Henkel, Karsten A1 - Flege, Jan Ingo T1 - Functional ultra-thin oxide films deposited by atomic layer deposition on structured substrates T2 - Verhandlungen der DPG - SurfaceScience21 N2 - In the last decades, atomic layer deposition (ALD) has gained prominence in the materials and surface science communities owing to its high potential for integration as a scalable process in microelectronics. ALD's largest strengths are its well-controlled layer-by-layer deposition and growth conformity on 3D structures. Yet, the ALD technique is also well known to lead to amorphous and defective, non-stoichiometric thin films, resulting in modified materials properties that may even preferentially be used in certain applications. To study these issues, we have developed an in-situ ALD reactor attached to an X-ray photoelectron spectroscopy (XPS) system, capable of switching between both pump and flow-type operation. This novel tool allows to cover the entire range of compounds and recipes used in ALD, thus clarifying the role of such defects at different deposition stages, growth conditions and film/substrate interfaces. To exemplify these sorts of studies, we show the deposition of Al2O3 5-10 nm films on nanostructured Si, and their use as substrates for functional CeOx ALD deposits. KW - Atomic layer deposition KW - sensors KW - structured substrates KW - in-situ X-ray photoelectron spectroscopy Y1 - 2021 UR - https://www.dpg-verhandlungen.de/year/2021/conference/surfacescience/part/o/session/74/contribution/5 VL - 2021 PB - Deutsche Physikalische Gesellschaft e.V. CY - Bad Honnef ER - TY - GEN A1 - Pérez-Bosch Quesada, Emilio A1 - Romero-Zaliz, Rocío A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Reuben, John A1 - Schubert, Markus Andreas A1 - Jiménez-Molinos, Francisco A1 - Roldán, Juan Bautista A1 - Wenger, Christian T1 - Toward Reliable Compact Modeling of Multilevel 1T-1R RRAM Devices for Neuromorphic Systems T2 - Electronics (MDPI) N2 - In this work, three different RRAM compact models implemented in Verilog-A are analyzed and evaluated in order to reproduce the multilevel approach based on the switching capability of experimental devices. These models are integrated in 1T-1R cells to control their analog behavior by means of the compliance current imposed by the NMOS select transistor. Four different resistance levels are simulated and assessed with experimental verification to account for their multilevel capability. Further, an Artificial Neural Network study is carried out to evaluate in a real scenario the viability of the multilevel approach under study. KW - RRAM KW - multilevel switching KW - compact modeling KW - Verilog-A Y1 - 2021 U6 - https://doi.org/10.3390/electronics10060645 SN - 2079-9292 VL - 10 IS - 6 ER - TY - GEN A1 - Pechmann, Stefan A1 - Mai, Timo A1 - Völkel, Matthias A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Pérez, Eduardo A1 - Perez-Bosch Quesada, Emilio A1 - Reichenbach, Marc A1 - Wenger, Christian A1 - Hagelauer, Amelie T1 - A Versatile, Voltage-Pulse Based Read and Programming Circuit for Multi-Level RRAM Cells T2 - Electronics N2 - In this work, we present an integrated read and programming circuit for Resistive Random Access Memory (RRAM) cells. Since there are a lot of different RRAM technologies in research and the process variations of this new memory technology often spread over a wide range of electrical properties, the proposed circuit focuses on versatility in order to be adaptable to different cell properties. The circuit is suitable for both read and programming operations based on voltage pulses of flexible length and height. The implemented read method is based on evaluating the voltage drop over a measurement resistor and can distinguish up to eight different states, which are coded in binary, thereby realizing a digitization of the analog memory value. The circuit was fabricated in the 130 nm CMOS process line of IHP. The simulations were done using a physics-based, multi-level RRAM model. The measurement results prove the functionality of the read circuit and the programming system and demonstrate that the read system can distinguish up to eight different states with an overall resistance ratio of 7.9. KW - RRAM KW - Multilevel switching KW - Programming circuit Y1 - 2021 U6 - https://doi.org/10.3390/electronics10050530 SN - 2079-9292 VL - 10 IS - 5 ER - TY - GEN A1 - Matbaechi Ettehad, Honeyeh A1 - Wenger, Christian T1 - Characterization and Separation of Live and Dead Yeast Cells Using CMOS-Based DEP Microfluidics T2 - Micromachines N2 - This study aims at developing a miniaturized CMOS integrated silicon-based microfluidic system, compatible with a standard CMOS process, to enable the characterization, and separation of live and dead yeast cells (as model bio-particle organisms) in a cell mixture using the DEP technique. DEP offers excellent benefits in terms of cost, operational power, and especially easy electrode integration with the CMOS architecture, and requiring label-free sample preparation. This can increase the likeliness of using DEP in practical settings. In this work the DEP force was generated using an interdigitated electrode arrays (IDEs) placed on the bottom of a CMOS-based silicon microfluidic channel. This system was primarily used for the immobilization of yeast cells using DEP. This study validated the system for cell separation applications based on the distinct responses of live and dead cells and their surrounding media. The findings confirmed the device’s capability for efficient, rapid and selective cell separation. The viability of this CMOS embedded microfluidic for dielectrophoretic cell manipulation applications and compatibility of the dielectrophoretic structure with CMOS production line and electronics, enabling its future commercially mass production. KW - dielectrophoresis KW - microfluidics KW - cell characterization KW - CMOS Y1 - 2021 U6 - https://doi.org/10.3390/mi12030270 SN - 2072-666X VL - 12 IS - 3 ER - TY - GEN A1 - Pérez, Eduardo A1 - Pérez-Ávila, Antonio Javier A1 - Romero-Zaliz, Rocío A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Pérez-Bosch Quesada, Emilio A1 - Roldan, Juan Bautista A1 - Jiménez-Molinos, Francisco A1 - Wenger, Christian T1 - Optimization of Multi-Level Operation in RRAM Arrays for In-Memory Computing T2 - Electronics (MDPI) N2 - Accomplishing multi-level programming in resistive random access memory (RRAM) arrays with truly discrete and linearly spaced conductive levels is crucial in order to implement synaptic weights in hardware-based neuromorphic systems. In this paper, we implemented this feature on 4-kbit 1T1R RRAM arrays by tuning the programming parameters of the multi-level incremental step pulse with verify algorithm (M-ISPVA). The optimized set of parameters was assessed by comparing its results with a non-optimized one. The optimized set of parameters proved to be an effective way to define non-overlapped conductive levels due to the strong reduction of the device-to-device variability as well as of the cycle-to-cycle variability, assessed by inter-levels switching tests and during 1k reset-set cycles. In order to evaluate this improvement in real scenarios, the experimental characteristics of the RRAM devices were captured by means of a behavioral model, which was used to simulate two different neuromorphic systems: an 8×8 vector-matrixmultiplication (VMM) accelerator and a 4-layer feedforward neural network for MNIST database recognition. The results clearly showed that the optimization of the programming parameters improved both the precision of VMM results as well as the recognition accuracy of the neural network in about 6% compared with the use of non-optimized parameters. KW - RRAM KW - Multilevel switching KW - In-memory computing Y1 - 2021 U6 - https://doi.org/10.3390/electronics10091084 SN - 2079-9292 VL - 10 IS - 9 ER - TY - GEN A1 - Milo, Valerio A1 - Anzalone, Francesco A1 - Zambelli, Cristian A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Ossorio, Óscar G. A1 - Olivo, Piero A1 - Wenger, Christian A1 - Ielmini, Daniele T1 - Optimized programming algorithms for multilevel RRAM in hardware neural networks T2 - IEEE International Reliability Physics Symposium (IRPS), 2021 N2 - A key requirement for RRAM in neural network accelerators with a large number of synaptic parameters is the multilevel programming. This is hindered by resistance imprecision due to cycle-to-cycle and device-to-device variations. Here, we compare two multilevel programming algorithms to minimize resistance variations in a 4-kbit array of HfO 2 RRAM. We show that gate-based algorithms have the highest reliability. The optimized scheme is used to implement a neural network with 9-level weights, achieving 91.5% (vs. software 93.27%) in MNIST recognition. KW - RRAM KW - Multilevel switching KW - neural network KW - memristive switching Y1 - 2021 SN - 978-1-7281-6894-4 U6 - https://doi.org/10.1109/IRPS46558.2021.9405119 SN - 1938-1891 ER - TY - GEN A1 - Petryk, Dmytro A1 - Dyka, Zoya A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Kabin, Ievgen A1 - Wenger, Christian A1 - Langendörfer, Peter T1 - Evaluation of the Sensitivity of RRAM Cells to Optical Fault Injection Attacks T2 - EUROMICRO Conference on Digital System Design (DSD 2020), Special Session: Architecture and Hardware for Security Applications (AHSA) Y1 - 2021 SN - 978-1-7281-9535-3 U6 - https://doi.org/10.1109/DSD51259.2020.00047 SN - 978-1-7281-9536-0 ER - TY - GEN A1 - Romero-Zaliz, Rocío A1 - Pérez, Eduardo A1 - Jimenez-Molinos, Francisco A1 - Wenger, Christian A1 - Roldan, Juan Bautista T1 - Influence of variability on the performance of HfO2 memristor-based convolutional neural networks T2 - Solid State Electronics N2 - A study of convolutional neural networks (CNNs) was performed to analyze the influence of quantization and variability in the network synaptic weights. Different CNNs were considered accounting for the number of convolutional layers, size of the filters in the convolutional layer, number of neurons in the final network layers and different sets of quantization levels. The conductance levels of fabricated 1T1R structures based on HfO2 memristors were considered as reference for four or eight level quantization processes at the inference stage of the CNNs, which were previous trained with the MNIST dataset. We also included the variability of the experimental conductance levels that was found to be Gaussian distributed and was correspondingly modeled for the synaptic weight implementation. KW - RRAM KW - neural network KW - HfO2 KW - memristive switching Y1 - 2021 U6 - https://doi.org/10.1016/j.sse.2021.108064 SN - 0038-1101 VL - 185 ER - TY - GEN A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Perez-Bosch Quesada, Emilio A1 - Wenger, Christian T1 - Variability and Energy Consumption Tradeoffs in Multilevel Programming of RRAM Arrays T2 - IEEE Transactions on Electron Devices N2 - Achieving a reliable multi-level programming operation in resistive random access memory (RRAM) arrays is still a challenging task. In this work, we assessed the impact of the voltage step value used by the programming algorithm on the device-to-device (DTD) variability of the current distributions of four conductive levels and on the energy consumption featured by programming 4-kbit HfO2-based RRAM arrays. Two different write-verify algorithms were considered and compared, namely, the incremental gate voltage with verify algorithm (IGVVA) and the incremental step pulse with verify algorithm (ISPVA). By using the IGVVA, a main trade-off has to be taken into account since reducing the voltage step leads to a smaller DTD variability at the cost of a strong increase in the energy consumption. Although the ISPVA can not reduce the DTD variability as much as the IGVVA, its voltage step can be decreased in order to reduce the energy consumption with almost no impact on the DTD variability. Therefore, the final decision on which algorithm to employ should be based on the specific application targeted for the RRAM array. KW - RRAM KW - Multilevel switching KW - HfO2 Y1 - 2021 U6 - https://doi.org/10.1109/TED.2021.3072868 SN - 0018-9383 SN - 1557-9646 VL - 68 IS - 6 SP - 2693 EP - 2698 ER - TY - GEN A1 - Ossorio, Óscar G. A1 - Vinuesa, Guillermo A1 - Garcia, Hector A1 - Sahelices, Benjamin A1 - Dueñas, Salvador A1 - Castán, Helena A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Wenger, Christian T1 - Performance Assessment of Amorphous HfO2-based RRAM Devices for Neuromorphic Applications T2 - ECS Transactions N2 - The use of thin layers of amorphous hafnium oxide has been shown to be suitable for the manufacture of Resistive Random-Access memories (RRAM). These memories are of great interest because of their simple structure and non-volatile character. They are particularly appealing as they are good candidates for substituting flash memories. In this work, the performance of the MIM structure that takes part of a 4 kbit memory array based on 1-transistor-1-resistance (1T1R) cells was studied in terms of control of intermediate states and cycle durability. DC and small signal experiments were carried out in order to fully characterize the devices, which presented excellent multilevel capabilities and resistive-switching behavior. KW - RRAM KW - resistive switching KW - HfO2 Y1 - 2021 U6 - https://doi.org/10.1149/10202.0029ecst SN - 1938-6737 SN - 1938-5862 VL - 102 IS - 2 SP - 29 EP - 35 ER - TY - GEN A1 - Mahmoodinezhad, Ali A1 - Morales, Carlos A1 - Naumann, Franziska A1 - Plate, Paul A1 - Meyer, Robert A1 - Janowitz, Christoph A1 - Henkel, Karsten A1 - Kot, Małgorzata A1 - Zöllner, Marvin Hartwig A1 - Wenger, Christian A1 - Flege, Jan Ingo T1 - Low-temperature atomic layer deposition of indium oxide thin films using trimethylindium and oxygen plasma T2 - Journal of Vacuum Science and Technology A N2 - Indium oxide (InxOy) thin films were deposited by plasma-enhanced atomic layer deposition (PEALD) using trimethylindium and oxygen plasma in a low-temperature range of 80–200 °C. The optical properties, chemical composition, crystallographic structure, and electrical characteristics of these layers were investigated by spectroscopic ellipsometry (SE), x-ray photoelectron spectroscopy (XPS), x-ray diffraction (XRD), as well as current-voltage and capacitance-voltage measurements. The SE results yielded a nearly constant growth rate of 0.56 Å per cycle and a thickness inhomogeneity of ≤1.2% across 4-in. substrates in the temperature range of 100–150 °C. The refractive index (at 632.8 nm) was found to be 2.07 for the films deposited at 150 °C. The PEALD-InxOy layers exhibit a direct (3.3 ± 0.2 eV) and an indirect (2.8 ± 0.1 eV) bandgap with an uptrend for both with increasing substrate temperature. Based on XPS characterization, all InxOy samples are free of carbon impurities and show a temperature-dependent off-stoichiometry indicating oxygen vacancies. XRD diffraction patterns demonstrate an onset of crystallization at 150 °C. Consistent with the optical, XPS, and XRD data, the films deposited at ≥150 °C possess higher electrical conductivity. Our findings prove that a low-temperature PEALD process of InxOy is feasible and promising for a high-quality thin-film deposition without chemical impurities on thermally fragile substrates. KW - Indium oxide KW - Plasma-enhanced atomic layer deposition KW - X-ray photoelectron spectroscopy KW - Ellipsometry KW - X-ray diffraction KW - Electrical properties Y1 - 2021 U6 - https://doi.org/10.1116/6.0001375 SN - 0734-2101 SN - 1520-8559 VL - 39 IS - 6 ER - TY - GEN A1 - Yun, Min Ju A1 - Lee, Doowon A1 - Kim, Sungho A1 - Wenger, Christian A1 - Kim, Hee-Dong T1 - A nonlinear resistive switching behaviors of Ni/HfO2/TiN memory structures for self-rectifying resistive switching memory T2 - Materials Characterization N2 - This work reports forming free/self-rectifying resistive switching characteristics and dependency of the top electrode (TE) of a crystalline HfO2-based resistive switching memory device. In the memory cells, nonlinear bipolar resistive switching characteristics, i.e., an asymmetric current-voltage curve like the Schottky diode, was observed. In addition, the device exhibits resistive switching behaviors without forming process, which makes it possible to switch the resistance state under ultra-low current levels of <10 nA. In addition, compared to the resistive switching of the proposed resistive switching memory devices with different TEs, the VSET was decreased when using TE with lower work function, and the height read margin was obtained in the sample with the Ni TE, covering over 56 × 56 arrays. Consequently, these results indicate that the interface control resistive switching properties in memory structures having the Schottky junction warrant the realization of selector-free resistive switching memory cells in a high-density crossbar array. KW - RRAM KW - resistive switching KW - HfO2 Y1 - 2021 U6 - https://doi.org/10.1016/j.matchar.2021.111578 SN - 1044-5803 VL - 182 ER - TY - GEN A1 - Perez-Bosch Quesada, Emilio A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Wenger, Christian T1 - Memristive-based in-memory computing: from device to large-scale CMOS integration T2 - Neuromorphic Computing and Engineering N2 - With the rapid emergence of in-memory computing systems based on memristive technology, the integration of such memory devices in large-scale architectures is one of the main aspects to tackle. In this work we present a study of HfO2-based memristive devices for their integration in large-scale CMOS systems, namely 200 mm wafers. The DC characteristics of single metal–insulator–metal devices are analyzed taking under consideration device-to-device variabilities and switching properties. Furthermore, the distribution of the leakage current levels in the pristine state of the samples are analyzed and correlated to the amount of formingless memristors found among the measured devices. Finally, the obtained results are fitted into a physic-based compact model that enables their integration into larger-scale simulation environments. KW - RRAM KW - memristive device KW - HfO2 Y1 - 2021 U6 - https://doi.org/10.1088/2634-4386/ac2cd4 SN - 2634-4386 VL - 1 IS - 2 ER - TY - GEN A1 - Baroni, Andrea A1 - Zambelli, Cristian A1 - Olivo, Piero A1 - Pérez, Eduardo A1 - Wenger, Christian A1 - Ielmini, Daniele T1 - Tackling the Low Conductance State Drift through Incremental Reset and Verify in RRAM Arrays T2 - 2021 IEEE International Integrated Reliability Workshop (IIRW), South Lake Tahoe, CA, USA, 10 December 2021 N2 - Resistive switching memory (RRAM) is a promising technology for highly efficient computing scenarios. RRAM arrays enabled the acceleration of neural networks for artificial intelligence and the creation of In-Memory Computing circuits. However, the arrays are affected by several issues materializing in conductance variations that might cause severe performance degradation in those applications. Among those, one is related to the drift of the low conductance states appearing immediately at the end of program and verify algorithms that are fundamental for an accurate Multi-level conductance operation. In this work, we tackle the issue by developing an Incremental Reset and Verify technique showing enhanced variability and reliability features compared with a traditional refresh-based approach. KW - RRAM KW - resistive switching KW - neural network Y1 - 2021 SN - 978-1-6654-1794-5 SN - 978-1-6654-1795-2 U6 - https://doi.org/10.1109/IIRW53245.2021.9635613 SN - 2374-8036 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - GEN A1 - Romero-Zaliz, Rocio A1 - Cantudo, Antonio A1 - Pérez, Eduardo A1 - Jimenez-Molinos, Francisco A1 - Wenger, Christian A1 - Roldan, Juan Bautista T1 - An Analysis on the Architecture and the Size of Quantized Hardware Neural Networks Based on Memristors T2 - Electronics (MDPI) N2 - We have performed different simulation experiments in relation to hardware neural networks (NN) to analyze the role of the number of synapses for different NN architectures in the network accuracy, considering different datasets. A technology that stands upon 4-kbit 1T1R ReRAM arrays, where resistive switching devices based on HfO2 dielectrics are employed, is taken as a reference. In our study, fully dense (FdNN) and convolutional neural networks (CNN) were considered, where the NN size in terms of the number of synapses and of hidden layer neurons were varied. CNNs work better when the number of synapses to be used is limited. If quantized synaptic weights are included, we observed thatNNaccuracy decreases significantly as the number of synapses is reduced; in this respect, a trade-off between the number of synapses and the NN accuracy has to be achieved. Consequently, the CNN architecture must be carefully designed; in particular, it was noticed that different datasets need specific architectures according to their complexity to achieve good results. It was shown that due to the number of variables that can be changed in the optimization of a NN hardware implementation, a specific solution has to be worked in each case in terms of synaptic weight levels, NN architecture, etc. KW - RRAM KW - memristive device KW - neural network Y1 - 2021 U6 - https://doi.org/10.3390/electronics10243141 SN - 2079-9292 VL - 10 IS - 24 ER - TY - GEN A1 - Stanke, Sandra A1 - Wenger, Christian A1 - Bier, Frank F. A1 - Hölzel, Ralph T1 - AC electrokinetic immobilization of influenza virus T2 - Electrophoresis N2 - The use of alternating current (AC) electrokinetic forces, like dielectrophoresis and AC electroosmosis, as a simple and fast method to immobilize sub-micrometer objects onto nanoelectrode arrays is presented. Due to its medical relevance, the influenza virus is chosen as a model organism. One of the outstanding features is that the immobilization of viral material to the electrodes can be achieved permanently, allowing subsequent handling independently from the electrical setup. Thus, by using merely electric fields, we demonstrate that the need of prior chemical surface modification could become obsolete. The accumulation of viral material over time is observed by fluorescence microscopy. The influences of side effects like electrothermal fluid flow, causing a fluid motion above the electrodes and causing an intensity gradient within the electrode array, are discussed. Due to the improved resolution by combining fluorescence microscopy with deconvolution, it is shown that the viral material is mainly drawn to the electrode edge and to a lesser extent to the electrode surface. Finally, areas of application for this functionalization technique are presented. KW - dielectrophoresis KW - immobilization KW - virus Y1 - 2022 U6 - https://doi.org/10.1002/elps.202100324 SN - 1522-2683 VL - 43 IS - 12 SP - 1309 EP - 1321 ER - TY - GEN A1 - Bischoff, Carl A1 - Leise, Jakob A1 - Perez-Bosch Quesada, Emilio A1 - Pérez, Eduardo A1 - Wenger, Christian A1 - Kloes, Alexander T1 - Implementation of device-to-device and cycle-to-cycle variability of memristive devices in circuit simulations T2 - Solid-State Electronics N2 - We present a statistical procedure for the extraction of parameters of a compact model for memristive devices. Thereby, in a circuit simulation the typical fluctuations of the current–voltage (I-V) characteristics from device-to-device (D2D) and from cycle-to-cycle (C2C) can be emulated. The approach is based on the Stanford model whose parameters play a key role to integrating D2D and C2C dispersion. The influence of such variabilities over the model’s parameters is investigated by using a fitting algorithm fed with experimental data. After this, the statistical distributions of the parameters are used in a Monte Carlo simulation to reproduce the I-V D2D and C2C dispersions which show a good agreement to the measured curves. The results allow the simulation of the on/off current variation for the design of RRAM cells or memristor-based artificial neural networks. KW - RRAM KW - circuit simulation KW - HfO2 Y1 - 2022 U6 - https://doi.org/10.1016/j.sse.2022.108321 SN - 0038-1101 VL - 194 ER - TY - GEN A1 - Mannocci, Piergiulio A1 - Baroni, Andrea A1 - Melacarne, Enrico A1 - Zambelli, Cristian A1 - Olivo, Piero A1 - Pérez, Eduardo A1 - Wenger, Christian A1 - Ielmini, Daniele T1 - In-Memory Principal Component Analysis by Crosspoint Array of Rresistive Switching Memory T2 - IEEE Nanotechnology Magazine N2 - In Memory Computing (IMC) is one of the most promising candidates for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and classification is principal component analysis (PCA), which heavily relies on matrixvector multiplications (MVM) for which classic von Neumann architectures are not optimized. Here, we provide the experimental demonstration of a new IMCbased PCA algorithm based on power iteration and deflation executed in a 4-kbit array of resistive switching random-access memory (RRAM). The classification accuracy of the Wisconsin Breast Cancer data set reaches 95.43%, close to floatingpoint implementation. Our simulations indicate a 250× improvement in energy efficiency compared to commercial GPUs, thus supporting IMC for energy-efficient ML in modern data-intensive computing. KW - RRAM KW - Multilevel switching KW - neural network Y1 - 2022 U6 - https://doi.org/10.1109/MNANO.2022.3141515 SN - 1932-4510 VL - 16 IS - 2 SP - 4 EP - 13 ER - TY - GEN A1 - Strobel, Carsten A1 - Alvarado Chavarin, Carlos A1 - Leszczynski, Sebastian A1 - Richter, Karola A1 - Knaut, Martin A1 - Reif, Johanna A1 - Völkel, Sandra A1 - Albert, Matthias A1 - Wenger, Christian A1 - Bartha, Johann Wolfgang A1 - Mikolajick, Thomas T1 - Improved Graphene-base Heterojunction Transistor with Different Collector Semi-conductors for High-frequency Applications T2 - Advanced Materials Letters N2 - A new kind of transistor device with a graphene monolayer embedded between two n-type silicon layers is fabricated and characterized. The device is called graphene-base heterojunction transistor (GBHT). The base-voltage controls the current of the device flowing from the emitter via graphene to the collector. The transit time for electrons passing by the ultrathin graphene layer is extremely short which makes the device very promising for high frequency RF-electronics. The output current of the device is saturated and clearly modulated by the base voltage. Further, the silicon collector of the GBHT is replaced by germanium to improve the device performance. This enabled the collector current to be increased by almost three orders of magnitude. Also, the common-emitter current gain (Ic/Ib) increased from 10-3 to approximately 0.3 for the newly designed device. However, the ON-OFF ratio of the improved germanium based GBHT has so far been rather low. Further optimizations are necessary in order to fully exploit the potential of the graphene-base heterojunction transistor. KW - Graphene KW - transistor Y1 - 2022 U6 - https://doi.org/10.5185/amlett.2022.011688 SN - 0976-3961 VL - 13 IS - 1 ER - TY - GEN A1 - Fritscher, Markus A1 - Knödtel, Johannes A1 - Mallah, Maen A1 - Pechmann, Stefan A1 - Perez-Bosch Quesada, Emilio A1 - Rizzi, Tommaso A1 - Wenger, Christian A1 - Reichenbach, Marc T1 - Mitigating the Effects of RRAM Process Variation on the Accuracy of Artifical Neural Networks T2 - Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021. Lecture Notes in Computer Science N2 - Weight storage is a key challenge in the efficient implementation of artificial neural networks. Novel memory technologies such as RRAM are able to greatly improve density and introduce non-volatility and multibit capabilities to this component of ANN accelerators. The usage of RRAM in this domain comes with downsides, mainly caused by cycle-to-cycle and device-to-device variability leading to erroneous readouts, greatly affecting digital systems. ANNs have the ability to compensate for this by their inherent redundancy and usually exhibit a gradual deterioration in the accuracy of the task at hand. This means, that slight error rates can be acceptable for weight storage in an ANN accelerator. In this work we link device-to-device variability to the accuracy of an ANN for such an accelerator. From this study, we can estimate how strongly a certain net is affected by a certain device parameter variability. This methodology is then used to present three mitigation strategies and to evaluate how they affect the reaction of the network to variability: a) Dropout Layers b) Fault-Aware Training c) Redundancy. These mitigations are then evaluated by their ability to improve accuracy and to lower hardware overhead by providing data for a real-word example. We improved this network’s resilience in such a way that it could tolerate double the variation in one of the device parameters (standard deviation of the oxide thickness can be 0.4 nm instead of 0.2 nm while maintaining sufficient accuracy.) KW - RRAM KW - memristive device KW - neural network Y1 - 2022 SN - 978-3-031-04579-0 SN - 978-3-031-04580-6 U6 - https://doi.org/10.1007/978-3-031-04580-6_27 SN - 0302-9743 SN - 1611-3349 SP - 401 EP - 417 PB - Springer ER - TY - GEN A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Pérez, Eduardo A1 - Lisker, Marco A1 - Schubert, Markus Andreas A1 - Perez-Bosch Quesada, Emilio A1 - Wenger, Christian A1 - Mai, Andreas T1 - Modulating the Filamentary-Based Resistive Switching Properties of HfO2 Memristive Devices by Adding Al2O3 Layers T2 - Electronics : open access journal N2 - The resistive switching properties of HfO2 based 1T-1R memristive devices are electrically modified by adding ultra-thin layers of Al2O3 into the memristive device. Three different types of memristive stacks are fabricated in the 130 nm CMOS technology of IHP. The switching properties of the memristive devices are discussed with respect to forming voltages, low resistance state and high resistance state characteristics and their variabilities. The experimental I–V characteristics of set and reset operations are evaluated by using the quantum point contact model. The properties of the conduction filament in the on and off states of the memristive devices are discussed with respect to the model parameters obtained from the QPC fit. KW - RRAM KW - HfO2 KW - filamentary switching Y1 - 2022 U6 - https://doi.org/10.3390/electronics11101540 SN - 2079-9292 VL - 11 IS - 10 ER - TY - GEN A1 - Glukhov, Artem A1 - Milo, Valerio A1 - Baroni, Andrea A1 - Lepri, Nicola A1 - Zambelli, Cristian A1 - Olivo, Piero A1 - Pérez, Eduardo A1 - Wenger, Christian A1 - Ielmini, Daniele T1 - Statistical model of program/verify algorithms in resistive-switching memories for in-memory neural network accelerators T2 - 2022 IEEE International Reliability Physics Symposium (IRPS) N2 - Resistive-switching random access memory (RRAM) is a promising technology for in-memory computing (IMC) to accelerate training and inference of deep neural networks (DNNs). This work presents the first physics-based statistical model describing (i) multilevel RRAM device program/verify (PV) algorithms by controlled set transition, (ii) the stochastic cycle-to-cycle (C2C) and device-to-device (D2D) variations within the array, and (iii) the impact of such imprecisions on the accuracy of DNN accelerators. The model can handle the full chain from RRAM materials/device parameters to the DNN performance, thus providing a valuable tool for device/circuit codesign of hardware DNN accelerators. KW - RRAM KW - Multilevel switching KW - neural network Y1 - 2022 SN - 978-1-6654-7950-9 SN - 978-1-6654-7951-6 U6 - https://doi.org/10.1109/IRPS48227.2022.9764497 SN - 2473-2001 SP - 3C.3-1 EP - 3C.3-7 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - GEN A1 - Janowitz, Christoph A1 - Mahmoodinezhad, Ali A1 - Kot, Małgorzata A1 - Morales, Carlos A1 - Naumann, Franziska A1 - Plate, Paul A1 - Zöllner, Marvin Hartwig A1 - Bärwolf, Florian A1 - Stolarek, David A1 - Wenger, Christian A1 - Henkel, Karsten A1 - Flege, Jan Ingo T1 - Toward controlling the Al2O3/ZnO interface properties by in situ ALD preparation T2 - Dalton Transactions N2 - An Al2O3/ZnO heterojunction was grown on a Si single crystal substrate by subsequent thermal and plasma-assisted atomic layer deposition (ALD) in situ. The band offsets of the heterointerface were then studied by consecutive removal of the layers by argon sputtering, followed by in situ X-ray photoelectron spectroscopy. The valence band maximum and conduction band minimum of Al2O3 are found to be 1.1 eV below and 2.3 eV above those of ZnO, resulting in a type-I staggered heterojunction. An apparent reduction of ZnO to elemental Zn in the interface region was detected in the Zn 2p core level and Zn L3MM Auger spectra. This suggests an interface formation different from previous models. The reduction of ZnO to Zn in the interface region accompanied by the creation of oxygen vacancies in ZnO results in an upward band bending at the interface. Therefore, this study suggests that interfacial properties such as the band bending as well as the valence and conduction band offsets should be in situ controllable to a certain extent by careful selection of the process parameters. KW - ALD heterojunction KW - band alignement KW - ZnO KW - Al2O3 KW - interface properties Y1 - 2022 U6 - https://doi.org/10.1039/D1DT04008A SN - 1477-9234 SN - 1477-9226 VL - 51 SP - 9291 EP - 9301 ER - TY - GEN A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Perez-Bosch Quesada, Emilio A1 - Wenger, Christian T1 - In-depth characterization of switching dynamics in amorphous HfO2 memristive arrays for the implementation of synaptic updating rules T2 - Japanese Journal of Applied Physics N2 - Accomplishing truly analog conductance modulation in memristive arrays is crucial in order to implement the synaptic plasticity in hardware-based neuromorphic systems. In this paper, such a feature was addressed by exploiting the inherent stochasticity of switching dynamics in amorphous HfO2 technology. A thorough statistical analysis of experimental characteristics measured in 4 kbit arrays by using trains of identical depression/potentiation pulses with different voltage amplitudes and pulse widths provided the key to develop two different updating rules and to define their optimal programming parameters. The first rule is based on applying a specific number of identical pulses until the conductance value achieves the desired level. The second one utilized only one single pulse with a particular amplitude to achieve the targeted conductance level. In addition, all the results provided by the statistical analysis performed may play an important role in understanding better the switching behavior of this particular technology. KW - RRAM KW - memristive device KW - HfO2 KW - memristive switching Y1 - 2022 U6 - https://doi.org/10.35848/1347-4065/ac6a3b SN - 0021-4922 VL - 61 SP - 1 EP - 7 ER -