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Ultralow-Power Machine Vision with Self-Powered Sensor Reservoir.
Jie Lao1, Mengge Yan1, Bobo Tian1,2
1Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Department of Electronics, East China Normal University, Shanghai, 200241, China.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|March 14, 2022
Summary
This study introduces a novel neuromorphic visual system using optoelectronic synapses for efficient in-sensor computing. The developed photonic devices enable ultralow-power machine vision with high accuracy for complex tasks.
Area of Science:
- Optoelectronics
- Neuromorphic Engineering
- Materials Science
Background:
- Neuromorphic visual systems offer reduced latency and energy consumption for in-sensor computing.
- Current systems face challenges in achieving efficient nonlinear photocurrent interactions for complex tasks.
Purpose of the Study:
- To develop a neuromorphic visual system with optoelectronic synapses for in-sensor computing.
- To enhance carrier dwell time for nonlinear photocurrent interactions necessary for reservoir computing.
- To demonstrate the system's capability in machine vision tasks.
Main Methods:
- Embedding a potential well on the Schottky barrier shoulder to extend photon-generated carrier dwell time.
- Designing self-powered Au/P(VDF-TrFE)/Cs2AgBiBr6/ITO devices for sensor reservoir construction.
- Evaluating the system's performance on static (face classification) and dynamic (vehicle flow recognition) vision tasks.
Main Results:
- Achieved effective extension of carrier dwell time, enabling nonlinear photocurrent interactions.
- Demonstrated high accuracy in machine vision: 99.97% for face classification and 100% for dynamic vehicle flow recognition.
- The in-sensor reservoir computing system exhibited near-zero energy consumption.
Conclusions:
- The developed neuromorphic visual system with optoelectronic synapses revolutionizes machine vision by enabling ultralow-power, high-accuracy in-sensor computing.
- The potential well integration strategy is key to achieving nonlinear optical signal processing.
- This work paves the way for next-generation photonic devices for energy-efficient machine vision applications.
