TY - JOUR
T1 - Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence
AU - Dai, Shilei
AU - Dai, Yahao
AU - Zhao, Zixuan
AU - Xia, Fangfang
AU - Li, Yang
AU - Liu, Youdi
AU - Cheng, Ping
AU - Strzalka, Joseph
AU - Li, Songsong
AU - Li, Nan
AU - Su, Qi
AU - Wai, Shinya
AU - Liu, Wei
AU - Zhang, Cheng
AU - Zhao, Ruoyu
AU - Yang, J. Joshua
AU - Stevens, Rick
AU - Xu, Jie
AU - Huang, Jia
AU - Wang, Sihong
N1 - Funding Information:
This work is supported by the US Office of Naval Research (N00014-21-1-2266 and N00014-21-1-2581), the National Science Foundation award DMR-2011854, and the start-up fund from the University of Chicago. F.X. and J.X. acknowledge Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. J.X. acknowledges the Center for Nanoscale Materials, a US Department of Energy Office of Science User Facility and supported by the US Department of Energy Office of Science, under contract DE-AC02-06CH11357. This research used resources of the Advanced Photon Source, a US Department of Energy Office of Science User Facility, operated for the Department of Energy Office of Science by Argonne National Laboratory under contract DE-AC02-06CH11357. We appreciate the kind discussion with Q. Xia at University of Massachusetts Amherst and the help from S. Gupta and T. Zhong at the University of Chicago.
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/10/5
Y1 - 2022/10/5
N2 - For leveraging wearable technologies to advance precision medicine, personalized and learning-based analysis of continuously acquired health data is indispensable, for which neuromorphic computing provides the most efficient implementation of artificial intelligence (AI) data processing. For realizing on-body neuromorphic computing, skin-like stretchability is required but has yet to be combined with the desired neuromorphic metrics, including linear symmetric weight update and sufficient state retention, for achieving high computing efficiency. Here, we report an intrinsically stretchable electrochemical transistor-based neuromorphic device, which provides a large number (>800) of states, linear/symmetric weight update, excellent switching endurance (>100 million), and good state retention (>104 s) together with the high stretchability of 100% strain. We further demonstrate a prototype neuromorphic array that can perform vector-matrix multiplication even at 100% strain and also the feasibility of implementing AI-based classification of health signals with a high accuracy that is minimally influenced by the stretched state of the neuromorphic hardware.
AB - For leveraging wearable technologies to advance precision medicine, personalized and learning-based analysis of continuously acquired health data is indispensable, for which neuromorphic computing provides the most efficient implementation of artificial intelligence (AI) data processing. For realizing on-body neuromorphic computing, skin-like stretchability is required but has yet to be combined with the desired neuromorphic metrics, including linear symmetric weight update and sufficient state retention, for achieving high computing efficiency. Here, we report an intrinsically stretchable electrochemical transistor-based neuromorphic device, which provides a large number (>800) of states, linear/symmetric weight update, excellent switching endurance (>100 million), and good state retention (>104 s) together with the high stretchability of 100% strain. We further demonstrate a prototype neuromorphic array that can perform vector-matrix multiplication even at 100% strain and also the feasibility of implementing AI-based classification of health signals with a high accuracy that is minimally influenced by the stretched state of the neuromorphic hardware.
KW - artificial intelligence
KW - MAP 6: Development
KW - neuromorphic computing
KW - organic electrochemical transistors
KW - stretchable electronics
UR - http://www.scopus.com/inward/record.url?scp=85138782287&partnerID=8YFLogxK
U2 - 10.1016/j.matt.2022.07.016
DO - 10.1016/j.matt.2022.07.016
M3 - Journal article
AN - SCOPUS:85138782287
SN - 2590-2393
VL - 5
SP - 3375
EP - 3390
JO - Matter
JF - Matter
IS - 10
ER -
