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5 October 2023 Electrical characterization and benchmarking of memory devices for energy efficient analog in-memory computing
Matthew J. Marinella, Patrick Xiao, Chris H. Bennett, William Wahby, Sapan Agarwal
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Proceedings Volume PC12651, Low-Dimensional Materials and Devices 2023; PC1265102 (2023) https://doi.org/10.1117/12.2677730
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
Abstract
Analog in-memory computing (AIMC) is an emerging paradigm that can enable energy efficient computing orders of magnitude beyond what is currently possible. Memory candidates for AIMC include SONOS (semiconductor oxide nitride oxide nitride), emerging resistive memory (ReRAM) and electrochemical memory (ECRAM). Electrical requirements for these memories are different than traditional digital memories in that the exact conductivity state of every device is used in every calculation. Effects including programming error and state drift are incorporated in the algorithm output. This new set of requirements has forced the development of a novel, holistic methodology for the electrical characterization and benchmarking of these devices. This talk will discuss these characterization and benchmarking methodology, and its application to SONOS, ReRAM, and ECRAM. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.
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Matthew J. Marinella, Patrick Xiao, Chris H. Bennett, William Wahby, and Sapan Agarwal "Electrical characterization and benchmarking of memory devices for energy efficient analog in-memory computing", Proc. SPIE PC12651, Low-Dimensional Materials and Devices 2023, PC1265102 (5 October 2023); https://doi.org/10.1117/12.2677730
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KEYWORDS
Analog electronics
Neural networks
Oxides
Binary data
Computer programming
Education and training
Electrical conductivity
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