Using new materials for brain-like computing: from fundamental mechanisms to high-performance devices (Conference Presentation)

Alberto Salleo; Tyler Quill

doi:10.1117/12.2633262

3 October 2022 Using new materials for brain-like computing: from fundamental mechanisms to high-performance devices (Conference Presentation)

Alberto Salleo, Tyler Quill

Author Affiliations +

Proceedings Volume PC12210, Organic and Hybrid Sensors and Bioelectronics XV; PC122100J (2022) https://doi.org/10.1117/12.2633262
Event: SPIE Organic Photonics + Electronics, 2022, San Diego, California, United States

Abstract

The brain can perform massively parallel information processing while consuming only ~1- 100 fJ per synaptic event. I will describe an electrochemical neuromorphic device that switches at low energy (~80 fJ), and displays a large number of distinct, non-volatile conductance states within a ~1 V operating range. The tunable resistance behaves very linearly, allowing blind updates in a neural network when operated with the proper access device. These devices also display outstanding endurance achieving over 109 switching events with very little degradation. I will describe our recent efforts at scaling and materials selection, allowing us to reach 20 ns write pulses and operation at high temperature (up to 120°C). In particular, we developed a fully lithographic process that allowed us to demonstrate sub-µm channel devices, opening the door to integration with Si driving circuitry. By carefully deuterating the electrolytes, we provide strong evidence that the secret to the high speed and low energy switching properties of these artificial synapses is the combination of electronic and protonic transport. Finally, we demonstrate that the working mechanism is quite general by fabricating and operating high-performance synapses based on MXenes. This generality is promising in terms of monolithic integration as MXenes can be chosen to be BEOL compatible with Si.

Conference Presentation

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Alberto Salleo and Tyler Quill "Using new materials for brain-like computing: from fundamental mechanisms to high-performance devices (Conference Presentation)", Proc. SPIE PC12210, Organic and Hybrid Sensors and Bioelectronics XV, PC122100J (3 October 2022); https://doi.org/10.1117/12.2633262

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KEYWORDS

Silicon

Switching

Back end of line

Brain

Data processing

Lithography

Neural networks

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