Reconfigurable perovskite nickelate electronics for artificial intelligence

Zhang, Hai-Tian; Park, Tae Joon; Islam, A. Nafiul; Tran, Dat J.; Manna, Sukriti; Wang, Qi; Mondal, Sandip; Yu, Haoming; Banik, Suvo; Cheng, Shaobo; Zhou, Hua; Gamage, Sampath; Mahapatra, Sayantan; Zhu, Yimei; Abate, Yohannes; Jiang, Nan; Sankaranarayanan, Subramanian S.; Sengupta, Abhronil; Teuscher, Christof; Ramanathan, Shriram

doi:10.1126/science.abj7943

Title: Reconfigurable perovskite nickelate electronics for artificial intelligence

Journal Article · Thu Feb 03 00:00:00 EST 2022 · Science

DOI:https://doi.org/10.1126/science.abj7943· OSTI ID:1898604

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Purdue Univ., West Lafayette, IN (United States)
Pennsylvania State Univ., University Park, PA (United States)
Santa Clara Univ., Santa Clara, CA (United States)
Argonne National Lab. (ANL), Argonne, IL (United States). Center for Nanoscale Materials; Univ. of Illinois, Chicago, IL (United States)
Brookhaven National Lab. (BNL), Upton, NY (United States). Condensed Matter Physics and Materials Science
Argonne National Lab. (ANL), Lemont, IL (United States). Advanced Photon Source (APS)
Univ. of Georgia, Athens, GA (United States)
Univ. of Illinois, Chicago, IL (United States)
Portland State Univ., OR (United States)

Reconfigurable devices offer the ability to program electronic circuits on demand. Here, in this work, we demonstrated on-demand creation of artificial neurons, synapses, and memory capacitors in post-fabricated perovskite NdNiO₃ devices that can be simply reconfigured for a specific purpose by single-shot electric pulses. The sensitivity of electronic properties of perovskite nickelates to the local distribution of hydrogen ions enabled these results. With experimental data from our memory capacitors, simulation results of a reservoir computing framework showed excellent performance for tasks such as digit recognition and classification of electrocardiogram heartbeat activity. Using our reconfigurable artificial neurons and synapses, simulated dynamic networks outperformed static networks for incremental learning scenarios. The ability to fashion the building blocks of brain-inspired computers on demand opens up new directions in adaptive networks.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); US Air Force Office of Scientific Research (AFOSR); National Science Foundation (NSF)

Grant/Contract Number:: SC0012704; SC0019273; FA9550-19-1-0351; ARO W911NF-19-2-0237; BCS-2031632; CCF-1955815; AC02-06CH11357; AC02-05CH11231; FA9559-16-1-0172; DMR-1904097; CHE-1944796

OSTI ID:: 1898604

Report Number(s):: BNL-223685-2022-JAAM; TRN: US2310912

Journal Information:: Science, Vol. 375, Issue 6580; ISSN 0036-8075

Publisher:: AAASCopyright Statement

Country of Publication:: United States

Language:: English

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