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Related Experiment Video

Updated: Aug 19, 2025

Optrode Array for Simultaneous Optogenetic Modulation and Electrical Neural Recording

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Published on: September 1, 2022

Optical synaptic devices with ultra-low power consumption for neuromorphic computing.

Chenguang Zhu^1,2, Huawei Liu^1,2, Wenqiang Wang^1,2

¹Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China.

Light, Science & Applications

|November 28, 2022

Summary

Researchers developed an ultra-low power artificial photonic synapse using a BP/CdS heterostructure. This breakthrough enables energy-efficient neuromorphic computing and achieves high accuracy in image recognition tasks.

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Area of Science:

Materials Science
Optoelectronics
Computer Engineering

Background:

Neuromorphic computing offers energy-efficient parallel processing for massive data.
Photonic synapses are crucial for artificial neural systems but face high power consumption challenges.

Purpose of the Study:

To develop an artificial photonic synapse with ultra-low power consumption.
To demonstrate its potential in energy-efficient neuromorphic vision systems.

Main Methods:

Fabrication of a BP/CdS heterostructure-based artificial photonic synapse.
Characterization of its optoelectronic properties, including negative light response and responsivity.
Simulation of a fully-connected optoelectronic neural network (FONN) for image recognition.

Main Results:

The device exhibited a remarkable negative light response with high responsivity (up to 4.1 × 10^8 A/W).
Achieved ultra-low average power consumption of 4.78 fJ per training process, the lowest reported.
Simulated FONN achieved a maximum image recognition accuracy of 94.1%.

Conclusions:

The BP/CdS heterostructure provides a novel concept for designing energy-efficient artificial photonic synapses.
This technology holds significant potential for advancing high-performance neuromorphic vision systems.