Memristive crossbar arrays for brain-inspired computing
May 19-23, 2019
While the speed-energy efficiency of traditional digital processors approach a plateau because of limitations in transistor scaling and the von Neumann architecture, computing systems augmented with emerging devices such as memristors offer an attractive solution. A memristor, also known as a resistance switch, is an electronic device whose internal resistance state is dependent on the history of the current and/or voltage it has experienced. With their working mechanisms based on ion migration, the switching dynamics and electrical behavior of memristors closely resemble those of biological synapses and neurons. Because of its small size and fast switching speed, a memristor consumes a small amount of energy to update the internal state (training). Built into large-scale crossbar arrays, memristors perform in-memory computing by utilizing physical laws, such as Ohm’s law for multiplication and Kirchhoff’s current law for accumulation. The current readout at all columns (inference) is finished simultaneously regardless of the array size, offering a huge parallelism and hence superior computing throughput. The ability to directly interface with analog signals from sensors, without analog/digital conversion, could further reduce the processing time and energy overhead.
We developed memristive devices based on foundry compatible materials such as silicon oxide and halfnium oxide [1,2]. We demonstrated two nanometer scalability  and eight layer stackbility  with these devices. Furthermore, we integrated the halfnium oxide memristors into large analog crossbar arrays for analog signal and image processing , and the implemented multilayer memristor neural networks for machine learning applications [6,7]. The crossbar arrays were also used for other applications such as hardware security .
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Qiangfei Xia, "Memristive crossbar arrays for brain-inspired computing" in "Semiconductor Technology for Ultra Large Scale Integrated Circuits and Thin Film Transistors VII", Yue Kuo, Texas A&M University, USA Junichi Murota, Tohoku University, Japan Yukiharu Uraoka, Nara Advanced Institute of Science and Technology, Japan Yasuhiro Fukunaka, Kyoto University, Japan Eds, ECI Symposium Series, (2019). https://dc.engconfintl.org/ulsic_tft_vii/19