JOURNAL ARTICLE

Ultralow-power optoelectronic synaptic transistors based on polyzwitterion dielectrics for in-sensor reservoir computing

Xiaosong WuShuhui ShiBaoshuai LiangYu DongRumeng YangRuiduan JiZhongrui WangWeiguo Huang

Year: 2024 Journal:   Science Advances Vol: 10 (16)Pages: eadn4524-eadn4524   Publisher: American Association for the Advancement of Science

Abstract

Bio-inspired transistor synapses use solid electrolytes to achieve low-power operation and rich synaptic behaviors via ion diffusion and trapping. While these neuromorphic devices hold great promise, they still suffer from challenges such as high leakage currents and power consumption, electrolysis risk, and irreversible conductance changes due to long-range ion migrations and permanent ion trapping. In addition, their response to light is generally limited because of “exciton-polaron quenching”, which restricts their potential in in-sensor neuromorphic visions. To address these issues, we propose replacing solid electrolytes with polyzwitterions, where the cation and anion are covalently concatenated via a flexible alkyl chain, thus preventing long-range ion migrations while inducing good photoresponses to the transistors via interfacial charge trapping. Our detailed studies reveal that polyzwitterion-based transistors exhibit optoelectronic synaptic behavior with ultralow-power consumption (~250 aJ per spike) and enable high-performance in-sensor reservoir computing, achieving 95.56% accuracy in perceiving the trajectory of moving basketballs.

Keywords:
Neuromorphic engineering Transistor Optoelectronics Materials science Ion Leakage (economics) Dielectric Nanotechnology Computer science Electrical engineering Voltage Artificial neural network Physics Engineering

Metrics

67
Cited By
24.73
FWCI (Field Weighted Citation Impact)
61
Refs
1.00
Citation Normalized Percentile
Is in top 1%
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Citation History

Topics

Advanced Memory and Neural Computing
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Analytical Chemistry and Sensors
Physical Sciences →  Chemical Engineering →  Bioengineering
Neural Networks and Reservoir Computing
Physical Sciences →  Computer Science →  Artificial Intelligence
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