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 -