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High-performance asymmetric electrode structured light-stimulated synaptic transistor for artificial neural networks
Yixin Ran1, Wanlong Lu1, Xin Wang1
1Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. guanghao.lu@mail.xjtu.edu.cn.
Materials Horizons
|July 25, 2023
Summary
Researchers developed an asymmetric electrode synaptic transistor (As-LSST) for efficient neuromorphic computing. This device demonstrates low power consumption and high performance for artificial neural networks and optical logic functions.
Area of Science:
- Materials Science
- Optoelectronics
- Neuromorphic Engineering
Background:
- Photonics neuromorphic computing offers low latency, power consumption, and high bandwidth.
- Asymmetric electrode transistors are gaining attention for their efficiency and optical response.
Purpose of the Study:
- To systematically research and analyze the mechanisms of intelligent optical synapses using asymmetric electrodes.
- To present a novel asymmetric electrode structure of a light-stimulated synaptic transistor (As-LSST).
Main Methods:
- Fabrication of As-LSST with a bulk heterojunction semiconductor layer.
- Characterization of electrical properties, photosensitivity, and synaptic functions (e.g., excitatory postsynaptic currents, paired-pulse facilitation, long-term memory).
- Implementation of optical logic functions, associative learning, and an artificial neural network (ANN).
Main Results:
- As-LSST demonstrated superior electrical properties and photosensitivity.
- Achieved ultra-low energy consumption (2.14 × 10⁻¹⁸ J) at a low drain voltage (1 × 10⁻⁷ V).
- Successfully implemented optical logic and associative learning, with an ANN achieving >97.5% handwritten number recognition.
Conclusions:
- The As-LSST offers a promising, easily accessible platform for neuromorphic computing.
- The asymmetric electrode design is key to the device's ultra-low energy consumption and high performance.
- This work paves the way for advanced intelligent electronic devices and future neuromorphic systems.
