TY - GEN A1 - Meer, Klaus A1 - Ziegler, Martin T1 - An explicit solution to Post's problem over the reals T2 - Journal of Complexity Y1 - 2008 SN - 0885-064x VL - 24 IS - 1 SP - 3 EP - 15 ER - TY - CHAP A1 - Meer, Klaus A1 - Ziegler, Martin ED - Kučera, Luděk T1 - Real Computational Universality: The word problem for a class of groups with infinite presentation T2 - Mathematical foundations of computer science 2007, 32nd international symposium, MFCS 2007, Český Krumlov, Czech Republic, August 26-31, 2007, proceedings Y1 - 2007 SN - 978-3-540-74455-9 SP - 726 EP - 737 PB - Springer CY - Berlin [u.a.] ER - TY - CHAP A1 - Meer, Klaus A1 - Ziegler, Martin ED - Beckmann, Arnold T1 - Uncomputability below the real halting problem T2 - Second Conference on Computability in Europe, CiE 2006, Swansea, UK, June 30 - July 5, 2006, proceedings. Y1 - 2006 SN - 978-3-540-35466-6 SP - 368 EP - 377 PB - Springer CY - Berlin ER - TY - CHAP A1 - Meer, Klaus A1 - Ziegler, Martin ED - Liskiewicz, Maciej T1 - An explicit solution to Post's problem over the reals T2 - Fundamentals of computation theory, 15th international symposium, FCT 2005, Lübeck, Germany, August 17 - 20, 2005 Y1 - 2005 SN - 3-540-28193-2 SP - 456 EP - 467 PB - Springer CY - Berlin ER - TY - GEN A1 - Meer, Klaus A1 - Ziegler, Martin T1 - Real Computational Universality: The word problem for a class of groups with infinite presentation Y1 - 2009 ER - 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 - 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 - TY - GEN A1 - Nikiruy, Kristina A1 - Pérez, Eduardo A1 - Baroni, Andrea A1 - Dorai Swamy Reddy, Keerthi 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 - Spetzler, Benjamin A1 - Fritscher, Markus A1 - Park, Seongae A1 - Kim, Nayoun A1 - Wenger, Christian A1 - Ziegler, Martin T1 - AI-driven model for optimized pulse programming of memristive devices T2 - APL Machine Learning N2 - Next-generation artificial intelligence (AI) hardware based on memristive devices offers a promising approach to reducing the increasingly large energy consumption of AI applications. However, programming memristive AI hardware to achieve a desired synaptic weight configuration remains challenging because it requires accurate and energy-efficient algorithms for selecting the optimal weight-update pulses. Here, we present a computationally efficient AI model for predicting the weight update of memristive devices and guiding device programming. The synaptic weight-update behavior of bilayer HfO2/TiO2 memristive devices is characterized over a range of pulse parameters to provide experimental data for the AI model. Three different artificial neural network (ANN) configurations are trained and evaluated regarding the amount of training data required for accurate predictions and the computational costs. Finally, we apply the model to an antipulse weight-update process to demonstrate its performance. The results show that accurate and computationally inexpensive predictions are possible with comparatively few datasets and small ANNs. The normalized weight-update processes are predicted with accuracies comparable with larger model architectures but require only 896 floating point operations and 8.33 nJ per inference. This makes the model a promising candidate for integration into AI-driven device controllers as a precise and energy-efficient solution for memristive device programming. KW - RRAM KW - Neural network KW - Device model Y1 - 2025 U6 - https://doi.org/10.1063/5.0251113 SN - 2770-9019 VL - 3 IS - 2 SP - 1 EP - 7 PB - AIP Publishing ER -