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Related Experiment Video

Updated: Sep 20, 2025

Optrode Array for Simultaneous Optogenetic Modulation and Electrical Neural Recording

Optrode Array for Simultaneous Optogenetic Modulation and Electrical Neural Recording

Published on: September 1, 2022

Artificial Optoelectronic Synapse Featuring Bidirectional Post-Synaptic Current for Compact and Energy-Efficient

Hogeun Ahn^1,2, Yena Kim³, Seunghwan Seo^2,4,5

¹Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.

Advanced Materials (Deerfield Beach, Fla.)

|May 28, 2025

Summary

This summary is machine-generated.

This study introduces a novel artificial optoelectronic synapse with bidirectional current flow, eliminating the need for paired devices in hardware neural networks (HW-NNs). This innovation enhances energy efficiency and compactness for brain-inspired computing systems.

Keywords:

brain‐inspired computing hardware neural network, artificial synapse, artificial optoelectronic synapse, asymmetric metal contacts, van‐der‐Waals layered materials

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Area of Science:

Materials Science
Neuroscience
Electrical Engineering

Background:

Conventional hardware neural networks (HW-NNs) utilize artificial synapses with unidirectional current flow, requiring differential pairs for weight core implementation.
This necessitates complex circuitry and increases energy consumption in neuromorphic computing.

Purpose of the Study:

To develop an artificial optoelectronic synapse capable of bidirectional post-synaptic current (I_PSC).
To eliminate the need for differential synapse pairs in HW-NNs.
To demonstrate enhanced energy efficiency and compactness in brain-inspired computing.

Main Methods:

Fabrication of an optoelectronic synapse with an asymmetric metal contact structure and a charge trapping/de-trapping layer (h-BN/weight control layer) integrated with a WSe₂ semiconductor channel.
Characterization of synaptic behaviors including long-term potentiation/depression and spike-timing-dependent plasticity.
Demonstration of a 3 × 2 artificial synapse array for multiply-accumulate operations and simulation of MNIST handwritten digit recognition.

Main Results:

The artificial synapse exhibits bidirectional I_PSC, removing the requirement for differential pairs.
The device successfully emulates key synaptic plasticity mechanisms.
Simulations on the MNIST dataset show competitive recognition rates with reduced energy consumption for weight updates.

Conclusions:

The developed bidirectional optoelectronic synapse offers a more compact and energy-efficient solution for HW-NNs.
This technology advances the development of practical brain-inspired computing systems.
The approach demonstrates significant potential for next-generation neuromorphic hardware.