@misc{DirkmannHansenZiegleretal., author = {Dirkmann, Sven and Hansen, Mirko and Ziegler, Martin and Kohlstedt, Hermann and Mussenbrock, Thomas}, title = {The Role of Ion Transport Phenomena in Memristive Double Barrier Devices}, series = {Scientific Reports}, volume = {6}, journal = {Scientific Reports}, issn = {2045-2322}, doi = {10.1038/srep35686}, pages = {35686}, language = {en} } @misc{DirkmannZieglerHansenetal., author = {Dirkmann, Sven and Ziegler, Martin and Hansen, Mirko and Kohlstedt, Hermann and Trieschmann, Jan and Mussenbrock, Thomas}, title = {Kinetic Simulation of Filament Growth Dynamics in Memristive Electrochemical Metallization Devices}, series = {Journal of Applied Physics}, volume = {118}, journal = {Journal of Applied Physics}, number = {21}, issn = {1089-7550}, doi = {10.1063/1.4936107}, pages = {214501}, language = {en} } @misc{HansenZieglerKolbergetal., author = {Hansen, Mirko and Ziegler, Martin and Kolberg, Lucas and Soni, Rohit and Dirkmann, Sven and Mussenbrock, Thomas and Kohlstedt, Hermann}, title = {A Double Barrier Memristive Device}, series = {Scientific Reports}, volume = {5}, journal = {Scientific Reports}, issn = {2045-2322}, doi = {10.1038/srep13753}, pages = {13753}, language = {en} } @misc{ZahariHansenMussenbrocketal., author = {Zahari, Finn and Hansen, Mirko and Mussenbrock, Thomas and Ziegler, Martin and Kohlstedt, Hermann}, title = {Pattern Recognition with TiOx-based Memristive Devices}, series = {AIMS Materials Science}, volume = {2}, journal = {AIMS Materials Science}, number = {3}, issn = {2372-0484}, doi = {10.3934/matersci.2015.3.203}, pages = {203 -- 216}, language = {en} } @misc{StrobelHansenDirkmannetal., author = {Strobel, Julian and Hansen, Mirko and Dirkmann, Sven and Neelisetty, Krishna Kanth and Ziegler, Martin and Haberfehlner, Georg and Popescu, Radian and Kothleitner, Gerald and Chakravadhanula, Venkata Sai Kiran and K{\"u}bel, Christian and Kohlstedt, Hermann and Mussenbrock, Thomas and Kienle, Lorenz}, title = {In Depth Nano Spectroscopic Analysis on Homogeneously Switching Double Barrier Memristive Devices}, series = {Journal of Applied Physics}, volume = {121}, journal = {Journal of Applied Physics}, number = {24}, issn = {1089-7550}, doi = {10.1063/1.4990145}, pages = {245307}, language = {en} } @misc{SolanDirkmannHansenetal., author = {Solan, Enver and Dirkmann, Sven and Hansen, Mirko and Schroeder, Dietmar and Kohlstedt, Hermann and Ziegler, Martin and Mussenbrock, Thomas and Ochs, Karlheinz}, title = {An Enhanced Lumped Element Electrical Model of the Double Barrier Memristive Device}, series = {Journal of physics : D, Applied physics}, volume = {50}, journal = {Journal of physics : D, Applied physics}, number = {19}, doi = {10.1088/1361-6463/aa69ae}, pages = {10}, language = {en} } @misc{ZahariSchlichtingStrobeletal., author = {Zahari, Finn and Schlichting, Felix and Strobel, Julian and Dirkmann, Sven and Cipo, Julia and Gauter, Sven and Trieschmann, Jan and Marquardt, Richard and Haberfehlner, Georg and Kothleitner, Gerald and Kienle, Lorenz and Mussenbrock, Thomas and Ziegler, Martin and Kersten, Holger and Kohlstedt, Hermann}, title = {Correlation between sputter deposition parameters and I-V characteristics in double-barrier memristive devices}, series = {Journal of Vacuum Science \& Technology}, volume = {37}, journal = {Journal of Vacuum Science \& Technology}, number = {6}, issn = {1520-8559}, doi = {10.1116/1.5119984}, pages = {061203}, language = {en} } @misc{SoltaniZarrinZahariMahadevaiahetal., author = {Soltani Zarrin, Pouya and Zahari, Finn and Mahadevaiah, Mamathamba Kalishettyhalli and P{\´e}rez, Eduardo and Kohlstedt, Hermann and Wenger, Christian}, title = {Neuromorphic on‑chip recognition of saliva samples of COPD and healthy controls using memristive devices}, series = {Scientific Reports}, volume = {10}, journal = {Scientific Reports}, issn = {2045-2322}, doi = {10.1038/s41598-020-76823-7}, abstract = {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.}, language = {en} } @misc{ZahariPerezMahadevaiahetal., author = {Zahari, Finn and P{\´e}rez, Eduardo and Mahadevaiah, Mamathamba Kalishettyhalli and Kohlstedt, Hermann and Wenger, Christian and Ziegler, Martin}, title = {Analogue pattern recognition with stochastic switching binary CMOS‑integrated memristive devices}, series = {Scientific Reports}, volume = {10}, journal = {Scientific Reports}, issn = {2045-2322}, doi = {10.1038/s41598-020-71334-x}, pages = {15}, abstract = {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.}, language = {en} }