1. Resistive Memory Process Optimization for High Resistance Switching Toward Scalable Analog Compute Technology for Deep Learning
- Author
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Steven Consiglio, Qingyun Yang, K. Tapily, Saraf Iqbal Rashid, Muthumanickam Sankarapandian, Paul C. Jamison, Tsunomura Takaaki, C. Catano, Robert D. Clark, T. Ando, R. Pujari, Vijay Narayanan, Hisashi Higuchi, Gerrit J. Leusink, Malte J. Rasch, R. Soave, Hongwen Yan, Ernest Y. Wu, Dexin Kong, Aelan Mosden, Peter Biolsi, Youngseok Kim, Robert R. Robison, O. van der Straten, D. Koty, S. McDermott, Soon-Cheon Seo, Hiroyuki Miyazoe, Son Nguyen, A. Gasasira, Nicole Saulnier, Wilfried Haensch, Sebastian Engelmann, C. S. Wajda, Ramachandran Muralidhar, and S. DeVries
- Subjects
010302 applied physics ,Physics ,business.industry ,Deep learning ,Process (computing) ,Topology ,01 natural sciences ,Omega ,Electronic, Optical and Magnetic Materials ,Resistive random-access memory ,Stack (abstract data type) ,0103 physical sciences ,Scalability ,Process optimization ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Voltage - Abstract
We demonstrate a novel process for building a Resistive RAM (ReRAM) stack which reduces the forming voltage ( $\text{V}_{\textit {form}}$ ) and increases the switching resistance, both characteristics that are important ingredients for the use of ReRAM in scalable analog compute for AI. Utilizing this process, we explore analog switching characteristics above 100k $\Omega $ and demonstrate 4-bit programming at Rmax $=1\text{M}\Omega $ . Utilizing the same writing characteristics, CIFAR-10 inference simulation shows 90% accuracy, comparable to the full precision model accuracy.
- Published
- 2021
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