@article{7e5263e718d0411a9c51eaad81ee8037,
title = "Reconfigurable perovskite nickelate electronics for artificial intelligence",
abstract = "Reconfigurable devices offer the ability to program electronic circuits on demand. In this work, we demonstrated on-demand creation of artificial neurons, synapses, and memory capacitors in post-fabricated perovskite NdNiO3devices 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.",
author = "Zhang, {Hai Tian} and Park, {Tae Joon} and Islam, {A. N.M.Nafiul} and Tran, {Dat S.J.} and Sukriti Manna and Qi Wang and Sandip Mondal and Haoming Yu and Suvo Banik and Shaobo Cheng and Hua Zhou and Sampath Gamage and Sayantan Mahapatra and Yimei Zhu and Yohannes Abate and Nan Jiang and Sankaranarayanan, {Subramanian K.R.S.} and Abhronil Sengupta and Christof Teuscher and Shriram Ramanathan",
note = "Funding Information: The analysis of the synaptic properties and related measurements were supported by Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C), an Energy Frontier Research Center (EFRC) funded by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), under award DE-SC0019273. The fabrication and pulsed field measurements were supported by the Air Force Office of Scientific Research (AFOSR) FA9550-19-1-0351 and ARO W911NF-19-2- 0237, respectively. A.N.M.N.I. and A.S.'s research was funded in part by the National Science Foundation (NSF) under grants BCS-2031632 and CCF-1955815. Use of the Center for Nanoscale Materials and Advanced Photon Source, both Office of Science user facilities, was supported by the DOE, Office of Science, BES, under contract DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under contract DE-AC02- 05CH11231. This material is based on work supported by the DOE, Office of Science, BES Data, Artificial Intelligence and Machine Learning at DOE Scientific User Facilities program. This work at BNL was supported by DOE-BES, Materials Sciences and Engineering Division under contract DE-SC0012704. This research used resources of the Advanced Photon Source, a DOE Office of Science User Facility, operated for the DOE Office of Science by Argonne National Laboratory under contract DE-AC02-06CH11357. Extraordinary facility operations were supported in part by the DOE Office of Science through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on the response to COVID-19, with funding provided by the Coronavirus CARES Act. S.G. and Y.A. acknowledge support from the AFOSR, grant FA9559-16-1-0172, and NSF under grant DMR- 1904097. S.M. and N.J. acknowledge support from NSF grant CHE-1944796. Publisher Copyright: {\textcopyright} 2022 American Association for the Advancement of Science. All rights reserved.",
year = "2022",
month = feb,
day = "4",
doi = "10.1126/science.abj7943",
language = "English (US)",
volume = "375",
journal = "Science",
issn = "0036-8075",
publisher = "American Association for the Advancement of Science",
number = "6580",
}