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 NdNiO 3 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 04, 2022
- Source ID
- 10.1126/science.abj7943
Entities
People
- A. N. M. Nafiul Islam
- Abhronil Sengupta
- Christof Teuscher
- Dat S. J. Tran
- Hai-Tian Zhang
- Haoming Yu
- Hua Zhou
- Nan Jiang
- Qi Wang
- Sampath Gamage
- Sandip Mondal
- Sayantan Mahapatra
- Shaobo Cheng
- Shriram Ramanathan
- Subramanian K R S Sankaranarayanan
- Sukriti Manna
- Suvo Banik
- Tae Joon Park
- Yimei Zhu
- Yohannes Abate
Organizations
- Argonne National Laboratory
- Brookhaven National Laboratory
- Pennsylvania State University
- Portland State University
- Purdue University
- Santa Clara University
- University of Georgia
- University of Illinois Urbana–Champaign
- University of Illinois at Chicago