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 - Dersch, Nadine A1 - Perez-Bosch Quesada, Emilio A1 - Perez, 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 - Perez, 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 - 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 - 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 - 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 - 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 -