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 -