TY - GEN A1 - Taoka, Noriyuki A1 - Capellini, Giovanni A1 - Driesch, Nils von den A1 - Buca, Dan A1 - Zaumseil, Peter A1 - Schubert, Markus Andreas A1 - Klesse, Wolfgang Matthias A1 - Montanari, Michele A1 - Schröder, Thomas T1 - Sn Migration Control at High Temperature Due to High Deposition Speed for Forming High Quality GeSn Layer T2 - Applied Physics Express Y1 - 2016 U6 - https://doi.org/10.7567/APEX.9.031201 SN - 1882-0786 SN - 1882-0778 VL - 9 IS - 3 SP - 031201 ER - TY - GEN A1 - Niu, Gang A1 - Capellini, Giovanni A1 - Schubert, Markus Andreas A1 - Niermann, Tore A1 - Zaumseil, Peter A1 - Katzer, Jens A1 - Krause, Hans-Michael A1 - Skibitzki, Oliver A1 - Lehmann, Michael A1 - Xie, Ya-Hong A1 - Känel, Hans von A1 - Schröder, Thomas T1 - Dislocation-free Ge nano-crystals via pattern dependent selective Ge heteroepitaxy on Si nano-tip wafers T2 - Scientific Reports Y1 - 2016 U6 - https://doi.org/10.1038/srep22709 SN - 2045-2322 VL - 6 SP - 22709 ER - TY - GEN A1 - Niu, Gang A1 - Calka, Pauline A1 - Walczyk, Christian A1 - Guha, Subhajit A1 - Fraschke, Mirko A1 - Fröhlich, K. A1 - Hamoumou, Philippe A1 - Gautier, Brice A1 - Alff, Lambert A1 - Schröder, Thomas T1 - Geometric conductive filament confinement by nanotips for resistive switching of HfO₂-RAM devices with high performance T2 - Scientific Reports Y1 - 2016 SN - 2045-2322 VL - 6 IS - 25757 ER - TY - GEN A1 - Ewert, Moritz A1 - Schmidt, Thomas A1 - Flege, Jan Ingo A1 - Heidmann, Inga A1 - Grzela, Tomasz A1 - Klesse, Wolfgang Matthias A1 - Förster, M. A1 - Aballe, Lucia A1 - Schröder, Thomas T1 - Morphology and Chemical Composition of Co Germanide islands on Ge(001): in-situ nanoscale insights into contact formation for Ge-based device technology T2 - Nanotechnology Y1 - 2016 SN - 1361-6528 SN - 0957-4484 VL - 27 IS - 32 SP - 325705 ER - TY - GEN A1 - Yang, Sang Mo A1 - Mazet, Lucie A1 - Okatan, M. Baris A1 - Jesse, Stephen A1 - Niu, Gang A1 - Schroeder, Thomas A1 - Schamm-Chardon, Sylvie A1 - Dubourdieu, Catherine A1 - Baddorf, Arthur P. A1 - Kalinin, Sergei V. T1 - Decoupling indirect topographic cross-talk in band excitation piezoresponse force microscopy imaging and spectroscopy T2 - Applied Physics Letters Y1 - 2016 U6 - https://doi.org/10.1063/1.4954276 SN - 1077-3118 VL - 108 IS - 25 SP - 252902 ER - TY - GEN A1 - Engelhard, M. A1 - Klemp, C. A1 - Behringer, M. A1 - Rudolph, A. A1 - Skibitzki, Oliver A1 - Zaumseil, Peter A1 - Schröder, Thomas T1 - Characterization of reclaimed GaAs substrates and investigation of reuse for thin film InGaAlP LED epitaxial growth T2 - Journal of Applied Physics Y1 - 2016 U6 - https://doi.org/10.1063/1.4955333 SN - 1089-7550 SN - 0021-8979 VL - 120 IS - 4 SP - 045301 ER - TY - GEN A1 - Dabrowski, Jarek Marek A1 - Lippert, Gunther A1 - Avila, J. A1 - Baringhaus, Jens A1 - Colambo, I. A1 - Dedkov, Yu. S. A1 - Herziger, Felix A1 - Lupina, Grzegorz A1 - Maultzsch, J. A1 - Schaffus, T. A1 - Schröder, Thomas A1 - Sowinska, Małgorzata A1 - Tegenkamp, Christoph A1 - Vignaud, D. A1 - Asensio, Maria Carmen T1 - Understanding the growth mechanism of graphene on Ge/Si(001) surfaces T2 - Scientific Reports Y1 - 2016 U6 - https://doi.org/10.1038/srep31639 SN - 2045-2322 IS - 6 SP - Art. 31639 ER - TY - GEN A1 - Niu, Gang A1 - Capellini, Giovanni A1 - Hatami, Fariba A1 - Bartolomeo, Antonio di A1 - Niermann, Tore A1 - Hameed, Hussein Emad A1 - Schubert, Markus Andreas A1 - Krause, Hans-Michael A1 - Zaumseil, Peter A1 - Skibitzki, Oliver A1 - Masselink, William T. A1 - Lehmann, Michael A1 - Xie, Ya-Hong A1 - Schröder, Thomas T1 - Selective Epitaxy of InP on Si and Rectification in Graphene/InP/Si Hybrid Structure T2 - ACS Applied Materials and Interfaces Y1 - 2016 U6 - https://doi.org/10.1021/acsami.6b09592 SN - 1944-8244 SN - 1944-8252 VL - 8 IS - 40 SP - 26948 EP - 26955 ER - TY - GEN A1 - Sharat, S. U. A1 - Joseph, Merin A1 - Vogel, Stefan A1 - Hildebrandt, E. A1 - Komissinskiy, P. A1 - Kurian, J. A1 - Schröder, Thomas A1 - Alff, Lambert T1 - Impact of Oxygen stoichiometry on electroforming and multiple switching modes in TiN/TaOₓ/Pt based ReRAM T2 - Applied Physics Letters Y1 - 2016 U6 - https://doi.org/10.1063/1.4965872 SN - 1077-3118 SN - 0003-6951 VL - 109 IS - 17 SP - 173503 ER - TY - GEN A1 - Skibitzki, Oliver A1 - Capellini, Giovanni A1 - Yamamoto, Yuji A1 - Zaumseil, Peter A1 - Schubert, Markus Andreas A1 - Schröder, Thomas A1 - Ballabio, Andrea A1 - Bergamaschini, Roberto A1 - Salvalaglio, Marco A1 - Miglio, Leo A1 - Montalenti, Francesco T1 - RP-CVD growth of isolated Ge crystals and suspended layers on micrometric Si pillars T2 - ACS Applied Materials and Interfaces Y1 - 2016 U6 - https://doi.org/10.1021/acsami.6b07694 SN - 1944-8244 SN - 1944-8252 VL - 8 IS - 39 SP - 26374 EP - 26380 ER - TY - GEN A1 - Schlykow, Viktoria A1 - Klesse, Wolfgang Matthias A1 - Niu, Gang A1 - Taoka, Noriyuki A1 - Yamamoto, Yuji A1 - Skibitzki, Oliver A1 - Barget, M. R. A1 - Zaumseil, Peter A1 - Känel, Hans von A1 - Schubert, Markus Andreas A1 - Capellini, Giovanni A1 - Schroeder, Thomas T1 - Selective growth of fully relaxed GeSn nano-islands by nanoheteroepitaxy on patterned Si(001) T2 - Applied Physics Letters Y1 - 2016 U6 - https://doi.org/10.1063/1.4967500 SN - 1077-3118 VL - 109 IS - 20 SP - 202102 ER - TY - GEN A1 - Bartolomeo, Antonio di A1 - Passacantando, Maurizio A1 - Niu, Gang A1 - Schlykow, Viktoria A1 - Lupina, Grzegorz A1 - Giubileo, Filippo A1 - Schröder, Thomas T1 - Observation of field emission from GeSn nanoparticles epitaxially grown on silicon nanotip arrays T2 - Nanotechnology Y1 - 2016 SN - 1361-6528 SN - 0957-4484 VL - 27 IS - 48 SP - 485707 ER - TY - GEN A1 - Lukosius, Mindaugas A1 - Lippert, Gunther A1 - Dabrowski, Jarek Marek A1 - Kitzmann, Julia A1 - Lisker, Marco A1 - Kulse, Philipp A1 - Krüger, Andreas A1 - Fursenko, Oksana A1 - Costina, Ioan A1 - Trusch, Andreas A1 - Yamamoto, Yuji A1 - Wolff, Andre A1 - Mai, Andreas A1 - Schröder, Thomas T1 - Graphene Synthesis and Processing on Ge Substrates T2 - ECS transactions Y1 - 2016 U6 - https://doi.org/10.1149/07508.0533ecst SN - 1938-6737 SN - 1938-5862 VL - 75 IS - 8 SP - 533 EP - 540 ER - TY - GEN A1 - Lukosius, Mindaugas A1 - Dabrowski, Jarek Marek A1 - Lisker, Marco A1 - Kitzmann, Julia A1 - Schulze, Sebastian A1 - Lippert, Gunther A1 - Fursenko, Oksana A1 - Yamamoto, Yuji A1 - Schubert, Markus Andreas A1 - Krause, Hans-Michael A1 - Wolff, Andre A1 - Mai, A. A1 - Schröder, Thomas A1 - Lupina, Grzegorz T1 - Metal-free, CVD Graphene synthesis on 200 mm Ge / Si(001) substrates T2 - ACS Applied Materials and Interfaces Y1 - 2016 U6 - https://doi.org/10.1021/acsami.6b11397 SN - 1944-8244 SN - 1944-8252 VL - 8 IS - 49 SP - 33786 EP - 33793 ER - TY - GEN A1 - Grzela, Tomasz A1 - Capellini, Giovanni A1 - Koczorowski, Wojciech A1 - Schubert, Markus Andreas A1 - Czajka, Ryszard A1 - Curson, Neil J. A1 - Heidmann, Inga A1 - Schmidt, Thomas A1 - Falta, Jens A1 - Schröder, Thomas T1 - Growth and evolution of nickel – germanide nanonstructures on Ge(001) T2 - Nanotechnology Y1 - 2015 SN - 1361-6528 SN - 0957-4484 VL - 26 IS - 38 SP - 385701 ER - TY - GEN A1 - Wang, Pu A1 - Xia, Ming A1 - Liang, Owen A1 - Sun, Ke A1 - Cipriano, Aaron F. A1 - Schröder, Thomas A1 - Liu, Huinan A1 - Xie, Ya-Hong T1 - Label-free Neurotransmitter SERS Detection by Graphene-Gold Nanopyramid Heterostructure T2 - Analytical Chemistry Y1 - 2015 U6 - https://doi.org/10.1021/acs.analchem.5b01560 SN - 0003-2700 SN - 1520-6882 VL - 87 IS - 20 SP - 10255 EP - 10261 ER - TY - GEN A1 - Bartolomeo, Antonio di A1 - Giubileo, Filippo A1 - Romeo, Francesco A1 - Sabatino, Paolo A1 - Carapella, Giovanni A1 - Iemmo, Laura A1 - Schröder, Thomas A1 - Lupina, Grzegorz T1 - Graphene field effect transistors with Niobium contacts and asymmetric transfer characteristics T2 - Nanotechnology Y1 - 2015 SN - 1361-6528 SN - 0957-4484 VL - 26 IS - 47 SP - 475202 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 - TY - GEN A1 - Perez-Avila, Antonio Javier A1 - Gonzalez-Cordero, Gerardo A1 - Perez, 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 - Perez, 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 - Perez, 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 - Romero-Zaliz, Rocío A1 - Perez, 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 - Dziallas, Giannino A1 - Fatemi, Adel A1 - Korndörfer, Falk A1 - Peczek, Anna A1 - Kissinger, Dietmar A1 - Zimmermann, Lars A1 - Malignaggi, Andrea A1 - Kahmen, Gerhard T1 - A Monolithically Integrated Optical Bandpass Receiver in 0.25μm SiGe BiCMOS Technology for Microwave-Photonic Applications T2 - 2020 IEEE Asian Solid-State Circuits Conference (A-SSCC), November 9-11, 2020 ONLINE Y1 - 2020 SN - 978-1-7281-8436-4 SN - 978-1-7281-8437-1 U6 - https://doi.org/10.1109/A-SSCC48613.2020.9336119 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 - Perez, 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 - Romero-Zaliz, Rocio A1 - Cantudo, Antonio A1 - Perez, 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 - Bischoff, Carl A1 - Leise, Jakob A1 - Perez-Bosch Quesada, Emilio A1 - Perez, 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 - Perez, 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 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Perez, 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 - Wen, Jianan A1 - Baroni, Andrea A1 - Perez, Eduardo A1 - Ulbricht, Markus A1 - Wenger, Christian A1 - Krstic, Milos T1 - Evaluating Read Disturb Effect on RRAM based AI Accelerator with Multilevel States and Input Voltages T2 - 2022 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT) N2 - RRAM technology is a promising candidate for implementing efficient AI accelerators with extensive multiply-accumulate operations. By scaling RRAM devices to the synaptic crossbar array, the computations can be realized in situ, avoiding frequent weights transfer between the processing units and memory. Besides, as the computations are conducted in the analog domain with high flexibility, applying multilevel input voltages to the RRAM devices with multilevel conductance states enhances the computational efficiency further. However, several non-idealities existing in emerging RRAM technology may degrade the reliability of the system. In this paper, we measured and investigated the impact of read disturb on RRAM devices with different input voltages, which incurs conductance drifts and introduces errors. The measured data are deployed to simulate the RRAM based AI inference engines with multilevel states. KW - RRAM KW - Multilevel switching KW - AI accelarator Y1 - 2022 SN - 978-1-6654-5938-9 SN - 978-1-6654-5937-2 U6 - https://doi.org/10.1109/DFT56152.2022.9962345 SN - 2765-933X SP - 1 EP - 6 ER - TY - GEN A1 - Buschek, Johannes A1 - Manthey, Kristian A1 - Eckhard, Andreas A1 - Reulke, Ralf T1 - In-Orbit Real-Time CMOS TDI Detector Validation and Control to Meet Constant Imaging Quality Over the Mission Life Time T2 - SPIE 12232, Earth Observing Systems XXVII, 1223205 (30 September 2022) Y1 - 2022 U6 - https://doi.org/10.1117/12.2631978 ER - TY - GEN A1 - Sütbas, Batuhan A1 - Kahmen, Gerhard T1 - A 7.2-mW V-Band Frequency Doubler with 14% Total Efficiency in 130-nm SiGe BiCMOS T2 - IEEE Microwave and Wireless Components Letters Y1 - 2022 U6 - https://doi.org/10.1109/LMWC.2022.3141557 SN - 1531-1309 SN - 1558-1764 VL - 32 IS - 6 SP - 579 EP - 582 ER - TY - GEN A1 - Akhtar, Fatima A1 - Dabrowski, Jaroslaw A1 - Lukose, Rasuole A1 - Wenger, Christian A1 - Lukosius, Mindaugas T1 - Chemical Vapor Deposition Growth of Graphene on 200 mm Ge (110)/Si Wafers and Ab Initio Analysis of Differences in Growth Mechanisms on Ge (110) and Ge (001) T2 - ACS Applied Materials & Interfaces N2 - For the fabrication of modern graphene devices, uniform growth of high-quality monolayer graphene on wafer scale is important. This work reports on the growth of large-scale graphene on semiconducting 8 inch Ge(110)/Si wafers by chemical vapor deposition and a DFT analysis of the growth process. Good graphene quality is indicated by the small FWHM (32 cm–1) of the Raman 2D band, low intensity ratio of the Raman D and G bands (0.06), and homogeneous SEM images and is confirmed by Hall measurements: high mobility (2700 cm2/Vs) and low sheet resistance (800 Ω/sq). In contrast to Ge(001), Ge(110) does not undergo faceting during the growth. We argue that Ge(001) roughens as a result of vacancy accumulation at pinned steps, easy motion of bonded graphene edges across (107) facets, and low energy cost to expand Ge area by surface vicinals, but on Ge(110), these mechanisms do not work due to different surface geometries and complex reconstruction. KW - Graphene KW - Chemical Vapor Deposition Y1 - 2023 U6 - https://doi.org/10.1021/acsami.3c05860 SN - 1944-8244 VL - 15 IS - 30 SP - 36966 EP - 36974 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 - Rizzi, Tommaso A1 - Baroni, Andrea A1 - Glukhov, Artem A1 - Bertozzi, Davide A1 - Wenger, Christian A1 - Ielmini, Daniele A1 - Zambelli, Cristian T1 - Process-Voltage-Temperature Variations Assessment in Energy-Aware Resistive RAM-Based FPGAs T2 - IEEE Transactions on Device and Materials Reliability N2 - Resistive Random Access Memory (RRAM) technology holds promises to improve the Field Programmable Gate Array (FPGA) performance, reduce the area footprint, and dramatically lower run-time energy requirements compared to the state-of-the-art CMOS-based products. However, the integration of RRAM in FPGAs is hindered by the high programming power consumption and by non-ideal behaviors of the device due to its stochastic nature that may overshadow the benefits in normal operation mode. To cope with these challenges, optimized programming strategies have to be investigated. In this work, we explore the impact that different procedures to set the device have on the run-time performance. Process, voltage, and temperature (PVT) variations as well as time-dependent drift effect of the RRAM device are considered in the assessment of 4T1R MUX designs characteristics. The comparison with tradition CMOS implementations reveals how the choice of the target resistive state and the programming algorithm are key design aspects to reduce the run-time delay and energy metrics, while at the same time improving the robustness against the different sources of variations. KW - RRAM KW - FPGA Y1 - 2023 U6 - https://doi.org/10.1109/TDMR.2023.3259015 SN - 1530-4388 VL - 23 IS - 3 SP - 328 EP - 336 ER - TY - GEN A1 - Perez, Eduardo A1 - Maldonado, David A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Perez-Bosch Quesada, Emilio A1 - Cantudo, Antonio A1 - Jimenez-Molinos, Francisco A1 - Wenger, Christian A1 - Roldan, Juan Bautista T1 - A comparison of resistive switching parameters for memristive devices with HfO2 monolayers and Al2O3/HfO2 bilayers at the wafer scale T2 - 14th Spanish Conference on Electron Devices (CDE 2023), Valencia, Spain, 06-08 June 2023 N2 - Memristive devices integrated in 200 mm wafers manufactured in 130 nm CMOS technology with two different dielectrics, namely, a HfO2 monolayer and an Al2O3/HfO2 bilayer, have been measured. The cycle-to-cycle (C2C) and device-todevice (D2D) variability have been analyzed at the wafer scale using different numerical methods to extract the set (Vset) and reset (Vreset) voltages. Some interesting differences between both technologies were found in terms of switching characteristics KW - RRAM Y1 - 2023 SN - 979-8-3503-0240-0 U6 - https://doi.org/10.1109/CDE58627.2023.10339417 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - GEN A1 - Capista, Daniele A1 - Lukose, Rasuole A1 - Majnoon, Farnaz A1 - Lisker, Marco A1 - Wenger, Christian A1 - Lukosius, Mindaugas T1 - Study on the metal -graphene contact resistance achieved with one -dimensional contact architecture T2 - IEEE Nanotechnology Materials and Devices Conference (NMDC 2023), Paestum, Italy, 22-25 October 2023 N2 - Graphene has always been considered as one of the materials with the greatest potential for the realization of improved microelectronic and photonic devices. But to actually reach its full potential in Si CMOS technology, graphene -based devices need to overcome different challenges. They do not only need to have better performances than standard devices, but they also need to be compatible with the production of standard Si based devices. To address the first challenge the main route requires the optimization of the contact resistance, that highly reduces the devices performance, while the second challenges requires the integration of graphene inside the standard production lines used for microelectronic. In this work we used an 8” wafer pilot -line to realize our devices and we studied the behavior of the contact resistance between metal and graphene obtained by one -dimensional contact architecture between the two materials. The contact resistance has been measured by means of Transmission Line Method (TLM) with several contact patterning. KW - Graphene Y1 - 2023 SN - 979-8-3503-3546-0 U6 - https://doi.org/10.1109/NMDC57951.2023.10343775 SP - 118 EP - 119 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - GEN A1 - Lukosius, Mindaugas A1 - Lukose, Rasuolė A1 - Lisker, Marco A1 - Dubey, P. K. A1 - Raju, A. I. A1 - Capista, Daniele A1 - Majnoon, Farnaz A1 - Mai, A. A1 - Wenger, Christian T1 - Developments of Graphene devices in 200 mm CMOS pilot line T2 - Proc. Nanotechnology Materials and Devices Conference (NMDC 2023),Paestum, Italy, 22-25 October 2023 N2 - Due to the unique electronic band structure, graphene has opened great potential to extend the functionality of a large variety of devices. Despite the significant progress in the fabrication of various graphene based microelectronic devices, the integration of graphene devices still lack the stability and compatibility with Si-technology processes. Therefore, the investigation and preparation of graphene devices in conditions resembling as close as possible the Si technology environment is of highest importance. This study aims to explore various aspects of graphene research on a 200mm pilot line, with a focus on simulations and fabrication of graphene modulator. To be more precise, it includes design and fabrication of the layouts, necessary mask sets, creation of the flows, fabrication, and measurements of the Gr modulators on 200 mm wafers. KW - Graphene Y1 - 2023 SN - 979-8-3503-3546-0 U6 - https://doi.org/10.1109/NMDC57951.2023.10343569 SP - 505 EP - 506 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - GEN A1 - Maldonado, David A1 - Cantudo, Antonio A1 - Perez, Eduardo A1 - Romero-Zaliz, Rocio A1 - Perez-Bosch Quesada, Emilio A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Jimenez-Molinos, Francisco A1 - Wenger, Christian A1 - Roldan, Juan Bautista T1 - TiN/Ti/HfO2/TiN Memristive Devices for Neuromorphic Computing: From Synaptic Plasticity to Stochastic Resonance T2 - Frontiers in Neuroscience N2 - We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature.It is shown that this effect is important and greatly depends on the noise statistical characteristics. KW - RRAM KW - Neural network Y1 - 2023 U6 - https://doi.org/10.3389/fnins.2023.1271956 SN - 1662-4548 VL - 17 ER - TY - GEN A1 - Perez, 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 - TY - GEN A1 - Dziallas, Giannino A1 - Fatemi, Adel A1 - Peczek, Anna A1 - Zimmermann, Lars A1 - Malignaggi, Andrea A1 - Kahmen, Gerhard T1 - A 56-Gb/s Optical Receiver with 2.08-µA Noise Monolithically Integrated into a 250-nm SiGe BiCMOS Technology T2 - IEEE Transactions on Microwave Theory and Techniques Y1 - 2022 U6 - https://doi.org/10.1109/TMTT.2021.3104838 SN - 0018-9480 SN - 1557-9670 VL - 70 IS - 1 ER - TY - GEN A1 - Iseini, Festim A1 - Malignaggi, Andrea A1 - Korndörfer, Falk A1 - Inac, Mesut A1 - Kahmen, Gerhard T1 - Lumped Ultra-Broadband Linear Driver in 130 nm SiGe SG13G3 Technology T2 - IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) Y1 - 2022 U6 - https://doi.org/10.1109/BCICTS53451.2022.10051703 ER - TY - GEN A1 - Kahmen, Gerhard T1 - SiGe BiCMOS as Enabling Technology for Next Generation RF & THz Systems T2 - EuMIC 2021 Abstract Cards, 16th European Microwave Integrated Circuits Conference (EuMIC), London, United Kingdom, 2022 Y1 - 2022 U6 - https://doi.org/10.23919/EuMIC50153.2022.9783893 SP - S. 21 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Strobel, Carsten A1 - Alvarado Chavarin, Carlos A1 - Knaut, Martin A1 - Völkel, Sandra A1 - Albert, Matthias A1 - Hiess, Andre A1 - Max, Benjamin A1 - Wenger, Christian A1 - Kirchner, Robert A1 - Mikolajick, Thomas T1 - High Gain Graphene Based Hot Electron Transistor with Record High Saturated Output Current Density T2 - Advanced Electronic Materials N2 - Hot electron transistors (HETs) represent an exciting frontier in semiconductor technology, holding the promise of high-speed and high-frequency electronics. With the exploration of two-dimensional materials such as graphene and new device architectures, HETs are poised to revolutionize the landscape of modern electronics. This study highlights a novel HET structure with a record output current density of 800 A/cm² and a high current gain α, fabricated using a scalable fabrication approach. The HET structure comprises two-dimensional hexagonal boron nitride (hBN) and graphene layers wet transferred to a germanium substrate. The combination of these materials results in exceptional performance, particularly in terms of the highly saturated output current density. The scalable fabrication scheme used to produce the HET opens up opportunities for large-scale manufacturing. This breakthrough in HET technology holds promise for advanced electronic applications, offering high current capabilities in a practical and manufacturable device. KW - Graphene KW - Transistor Y1 - 2024 U6 - https://doi.org/10.1002/aelm.202300624 SN - 2199-160X VL - 10 IS - 2 ER - TY - GEN A1 - Pechmann, Stefan A1 - Perez, Eduardo A1 - Wenger, Christian A1 - Hagelauer, Amelie T1 - A current mirror Based read circuit design with multi-level capability for resistive switching deviceb T2 - 2024 International Conference on Electronics, Information, and Communication (ICEIC) N2 - This paper presents a read circuit design for resistive memory cells based on current mirrors. The circuit utilizes high-precision current mirrors and reference cells to determine the state of resistive memory using comparators. It offers a high degree in adaptability in terms of both resistance range and number of levels. Special emphasis was put on device protection to prevent accidental programming of the memory during read operations. The realized circuit can resolve eight states with a resolution of up to 1 k Ω, realizing a digitization of the analog memory information. Furthermore, the integration in a complete memory macro is shown. The circuit was realized in a 130 nm-process but can easily be adapted to other processes and resistive memory technologies. KW - RRAM KW - memristive device Y1 - 2024 SN - 979-8-3503-7188-8 U6 - https://doi.org/10.1109/ICEIC61013.2024.10457188 SN - 2767-7699 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - GEN A1 - Nikiruy, Kristina A1 - Perez, Eduardo A1 - Baroni, Andrea A1 - Reddy, Keerthi Dorai Swamy A1 - Pechmann, Stefan A1 - Wenger, Christian A1 - Ziegler, Martin T1 - Blooming and pruning: learning from mistakes with memristive synapses T2 - Scientific Reports N2 - AbstractBlooming and pruning is one of the most important developmental mechanisms of the biological brain in the first years of life, enabling it to adapt its network structure to the demands of the environment. The mechanism is thought to be fundamental for the development of cognitive skills. Inspired by this, Chialvo and Bak proposed in 1999 a learning scheme that learns from mistakes by eliminating from the initial surplus of synaptic connections those that lead to an undesirable outcome. Here, this idea is implemented in a neuromorphic circuit scheme using CMOS integrated HfO2-based memristive devices. The implemented two-layer neural network learns in a self-organized manner without positive reinforcement and exploits the inherent variability of the memristive devices. This approach provides hardware, local, and energy-efficient learning. A combined experimental and simulation-based parameter study is presented to find the relevant system and device parameters leading to a compact and robust memristive neuromorphic circuit that can handle association tasks. KW - RRAM KW - Neural network Y1 - 2024 U6 - https://doi.org/10.1038/s41598-024-57660-4 SN - 2045-2322 VL - 14 IS - 1 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 - Kloes, Alexander A1 - Bischoff, Carl A1 - Leise, Jakob A1 - Perez-Bosch Quesada, Emilio A1 - Wenger, Christian A1 - Perez, Eduardo T1 - Stochastic switching of memristors and consideration in circuit simulation T2 - Solid State Electronics N2 - We explore the stochastic switching of oxide-based memristive devices by using the Stanford model for circuit simulation. From measurements, the device-to-device (D2D) and cycle-to-cycle (C2C) statistical variation is extracted. In the low-resistive state (LRS) dispersion by D2D variability is dominant. In the high-resistive state (HRS) C2C dispersion becomes the main source of fluctuation. A statistical procedure for the extraction of parameters of the compact model is presented. Thereby, in a circuit simulation the typical D2D and C2C fluctuations of the current–voltage (I-V) characteristics can be emulated by extracting statistical parameters of key model parameters. 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 memory cells or can be used to emulate the synaptic behavior of these devices in artificial neural networks realized by a crossbar array of memristors. KW - RRAM KW - memristive device KW - variability Y1 - 2023 U6 - https://doi.org/10.1016/j.sse.2023.108606 SN - 0038-1101 VL - 201 ER - TY - GEN A1 - Prüfer, Mareike A1 - Wenger, Christian A1 - Bier, Frank F. A1 - Laux, Eva-Maria A1 - Hölzel, Ralph T1 - Activity of AC electrokinetically immobilized horseradish peroxidase T2 - Electrophoresis N2 - Dielectrophoresis(DEP) is an AC electrokinetic effect mainly used to manipulate cells.Smaller particles,like virions,antibodies,enzymes,andevendyemolecules can be immobilized by DEP as well. In principle, it was shown that enzymesare active after immobilization by DEP, but no quantification of the retainedactivity was reported so far. In this study, the activity of the enzyme horseradishperoxidase (HRP) is quantified after immobilization by DEP. For this, HRP is immobilized on regular arrays of titanium nitride ring electrodes of 500 nm diameter and 20 nm widths. The activity of HRP on the electrode chip is measured with a limit of detection of 60 fg HRP by observing the enzymatic turnover of Amplex Red and H2O2 to fluorescent resoruf in by fluorescence microscopy. The initial activity of the permanently immobilized HRP equals up to 45% of the activity that can be expected for an ideal monolayer of HRP molecules on all electrodes of the array. Localization of the immobilizate on the electrodesis accomplished by staining with the fluorescent product of the enzyme reac-tion.The high residual activity of enzymes after AC field induced immobilization shows the method’s suitability for biosensing and research applications. KW - dielectrophoresis KW - immobilization KW - nanoelectrodes Y1 - 2022 U6 - https://doi.org/10.1002/elps.202200073 SN - 1522-2683 VL - 43 IS - 18-19 SP - 1920 EP - 1933 ER -