| Literature DB >> 35113713 |
Hai-Tian Zhang1, Tae Joon Park1, A N M Nafiul Islam2, Dat S J Tran3, Sukriti Manna4,5, Qi Wang1, Sandip Mondal1, Haoming Yu1, Suvo Banik4,5, Shaobo Cheng6, Hua Zhou7, Sampath Gamage8, Sayantan Mahapatra9, Yimei Zhu6, Yohannes Abate8, Nan Jiang9, Subramanian K R S Sankaranarayanan4,5, Abhronil Sengupta2, Christof Teuscher10, Shriram Ramanathan1.
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 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.Entities:
Year: 2022 PMID: 35113713 DOI: 10.1126/science.abj7943
Source DB: PubMed Journal: Science ISSN: 0036-8075 Impact factor: 47.728