TY - GEN A1 - Dirkmann, Sven A1 - Hansen, Mirko A1 - Ziegler, Martin A1 - Kohlstedt, Hermann A1 - Mussenbrock, Thomas T1 - The Role of Ion Transport Phenomena in Memristive Double Barrier Devices T2 - Scientific Reports Y1 - 2016 U6 - https://doi.org/10.1038/srep35686 SN - 2045-2322 VL - 6 SP - 35686 ER - TY - GEN A1 - Dirkmann, Sven A1 - Ziegler, Martin A1 - Hansen, Mirko A1 - Kohlstedt, Hermann A1 - Trieschmann, Jan A1 - Mussenbrock, Thomas T1 - Kinetic Simulation of Filament Growth Dynamics in Memristive Electrochemical Metallization Devices T2 - Journal of Applied Physics Y1 - 2015 U6 - https://doi.org/10.1063/1.4936107 SN - 1089-7550 SN - 0021-8979 VL - 118 IS - 21 SP - 214501 ER - TY - GEN A1 - Hansen, Mirko A1 - Ziegler, Martin A1 - Kolberg, Lucas A1 - Soni, Rohit A1 - Dirkmann, Sven A1 - Mussenbrock, Thomas A1 - Kohlstedt, Hermann T1 - A Double Barrier Memristive Device T2 - Scientific Reports Y1 - 2015 U6 - https://doi.org/10.1038/srep13753 SN - 2045-2322 VL - 5 SP - 13753 ER - TY - GEN A1 - Zahari, Finn A1 - Hansen, Mirko A1 - Mussenbrock, Thomas A1 - Ziegler, Martin A1 - Kohlstedt, Hermann T1 - Pattern Recognition with TiOx-based Memristive Devices T2 - AIMS Materials Science Y1 - 2015 U6 - https://doi.org/10.3934/matersci.2015.3.203 SN - 2372-0484 VL - 2 IS - 3 SP - 203 EP - 216 ER - TY - GEN A1 - Strobel, Julian A1 - Hansen, Mirko A1 - Dirkmann, Sven A1 - Neelisetty, Krishna Kanth A1 - Ziegler, Martin A1 - Haberfehlner, Georg A1 - Popescu, Radian A1 - Kothleitner, Gerald A1 - Chakravadhanula, Venkata Sai Kiran A1 - Kübel, Christian A1 - Kohlstedt, Hermann A1 - Mussenbrock, Thomas A1 - Kienle, Lorenz T1 - In Depth Nano Spectroscopic Analysis on Homogeneously Switching Double Barrier Memristive Devices T2 - Journal of Applied Physics Y1 - 2017 U6 - https://doi.org/10.1063/1.4990145 SN - 1089-7550 SN - 0021-8979 VL - 121 IS - 24 SP - 245307 ER - TY - GEN A1 - Solan, Enver A1 - Dirkmann, Sven A1 - Hansen, Mirko A1 - Schroeder, Dietmar A1 - Kohlstedt, Hermann A1 - Ziegler, Martin A1 - Mussenbrock, Thomas A1 - Ochs, Karlheinz T1 - An Enhanced Lumped Element Electrical Model of the Double Barrier Memristive Device T2 - Journal of physics : D, Applied physics Y1 - 2017 U6 - https://doi.org/10.1088/1361-6463/aa69ae VL - 50 IS - 19 ER - TY - GEN A1 - Zahari, Finn A1 - Schlichting, Felix A1 - Strobel, Julian A1 - Dirkmann, Sven A1 - Cipo, Julia A1 - Gauter, Sven A1 - Trieschmann, Jan A1 - Marquardt, Richard A1 - Haberfehlner, Georg A1 - Kothleitner, Gerald A1 - Kienle, Lorenz A1 - Mussenbrock, Thomas A1 - Ziegler, Martin A1 - Kersten, Holger A1 - Kohlstedt, Hermann T1 - Correlation between sputter deposition parameters and I-V characteristics in double-barrier memristive devices T2 - Journal of Vacuum Science & Technology Y1 - 2019 U6 - https://doi.org/10.1116/1.5119984 SN - 1520-8559 VL - 37 IS - 6 SP - 061203 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 - 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 -