Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence

Dai, Shilei; Dai, Yahao; Zhao, Zixuan; Xia, Fangfang; Li, Yang; Liu, Youdi; Cheng, Ping; Strzalka, Joseph; Li, Songsong; Li, Nan; Su, Qi; Wai, Shinya; Liu, Wei; Zhang, Cheng; Zhao, Ruoyu; Yang, J. Joshua; Stevens, Rick; Xu, Jie; Huang, Jia; Wang, Sihong

doi:10.1016/j.matt.2022.07.016

Title: Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence

Journal Article · 03 August 2022 · Matter (Online)

DOI: https://doi.org/10.1016/j.matt.2022.07.016 · OSTI ID:1909343

Dai, Shilei ^[1]; Dai, Yahao ^[2]; Zhao, Zixuan ^[3]; Xia, Fangfang ^[3]; Li, Yang ^[2]; Liu, Youdi ^[2]; Cheng, Ping ^[2]; Strzalka, Joseph ^[4]; Li, Songsong ^[2]; Li, Nan ^[2]; Su, Qi ^[2]; Wai, Shinya ^[2]; Liu, Wei ^[2]; Zhang, Cheng ^[2]; Zhao, Ruoyu ^[5]; Yang, J. Joshua ^[5]; Stevens, Rick ^[3]; Xu, Jie ^[4]; Huang, Jia ^[6];

^[3]

Univ. of Chicago, IL (United States); Tongji University, Shanghai (China)
Univ. of Chicago, IL (United States)
Univ. of Chicago, IL (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States)
Univ. of Southern California, Los Angeles, CA (United States)
Tongji University, Shanghai (China)

For leveraging wearable technologies to advance precision medicine, personalized and learning-based analysis of continuously acquired health data is indispensable, for which neuromorphic computing could provide the most efficient implementation of artificial intelligence (AI) data processing. For realizing on-body neuromorphic computing, skin-like stretchability is required, but yet to be combined with the suite of desired neuromorphic metrics, including linear, symmetric weight update, and sufficient state retention, for achieving high computing efficiency. Here, we report an intrinsically stretchable neuromorphic device based on an electrochemical transistor, which provides a large number (>800) of states, linear/symmetric weight update, excellent switching endurance (>100 million), good state retention (>10⁴ s), together with high stretchability of 100% strain. Further integration into a prototype array successfully realized the implementation of vector-matrix multiplication even at 100% strain. Finally, we demonstrate the feasibility of implementing AI-based classification of health signals (as exemplified by electrocardiograms) with a high accuracy that is minimally influenced by the stretched state of the neuromorphic hardware. Finally, this work breaks the ground for combining AI data analysis into skin-like wearable electronics for achieving human-integrated/mimetic intelligent systems.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: National Science Foundation (NSF); US Department of the Navy, Office of Naval Research (ONR); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1909343

Journal Information:: Matter (Online), Journal Name: Matter (Online) Journal Issue: 10 Vol. 5; ISSN 2590-2385

Publisher:: Cell Press/ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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