A Rational Design of All-Optically Controlled Optoelectronic Synapses with Bidirectional STP-to-LTP Learning Capabilities

An all-optically controlled optoelectronic synapse can regulate the synaptic plasticity purely by light, leveraging the advantages of light signals in speed, bandwidth, and parallelism. Currently, optoelectronic synapses can only emulate limited functionalities of biological synapses, such as generating positive or negative photocurrents to mimic the excitatory or inhibitory postsynaptic potential, while the emulation of the more complex learning plasticity is often missing. In contrast, the biological synapse in the hippocampus can generate long-term plasticity (LTP) after frequent stimuli, which is closely related to the crucial learning behaviors and memory capabilities of human beings. In this work, an optoelectronic synapse with bidirectional learning plasticity is rationally designed by TCAD, demonstrating bidirectional short-term plasticity (STP) to LTP transition after high-frequency light stimulation. This optoelectronic synapse adopts a distinct phototransistor architecture featuring two semiconductor floating gates. Both short-term and long-term photocurrents controlled by stimulation frequency can be induced by exploiting the carrier generation-recombination dynamics and the mutual capacitive coupling between the adjacent floating gates. An all-optically controlled optoelectronic synapse with bidirectional STP-to-LTP learning plasticity is enabled by designing a double-sided dual-floating-gate phototransistor, providing an efficient all-optically driven hardware platform for potential neuromorphic computing applications.

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Publication Details

Journal
ACS Applied Electronic Materials
Published
2026-04-21
DOI
https://doi.org/10.1021/acsaelm.6c00013
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
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article

A Rational Design of All-Optically Controlled Optoelectronic Synapses with Bidirectional STP-to-LTP Learning Capabilities

S Q Wang, Zhigang Zang, Qingkai Qian, Wei Zhang et al.
ACS Applied Electronic Materials
Neural Networks and Reservoir Computing
article

A Rational Design of All-Optically Controlled Optoelectronic Synapses with Bidirectional STP-to-LTP Learning Capabilities

S Q Wang, Zhigang Zang, Qingkai Qian, Wei Zhang, Zeyu Hu, Tingxiang Zhang
article en

Abstract

An all-optically controlled optoelectronic synapse can regulate the synaptic plasticity purely by light, leveraging the advantages of light signals in speed, bandwidth, and parallelism. Currently, optoelectronic synapses can only emulate limited functionalities of biological synapses, such as generating positive or negative photocurrents to mimic the excitatory or inhibitory postsynaptic potential, while the emulation of the more complex learning plasticity is often missing. In contrast, the biological synapse in the hippocampus can generate long-term plasticity (LTP) after frequent stimuli, which is closely related to the crucial learning behaviors and memory capabilities of human beings. In this work, an optoelectronic synapse with bidirectional learning plasticity is rationally designed by TCAD, demonstrating bidirectional short-term plasticity (STP) to LTP transition after high-frequency light stimulation. This optoelectronic synapse adopts a distinct phototransistor architecture featuring two semiconductor floating gates. Both short-term and long-term photocurrents controlled by stimulation frequency can be induced by exploiting the carrier generation-recombination dynamics and the mutual capacitive coupling between the adjacent floating gates. An all-optically controlled optoelectronic synapse with bidirectional STP-to-LTP learning plasticity is enabled by designing a double-sided dual-floating-gate phototransistor, providing an efficient all-optically driven hardware platform for potential neuromorphic computing applications.

ACS Applied Electronic Materials
Chongqing University (CN), Integrated Optoelectronics (Norway) (NO)
Openalex Percentile: Top 9%
Neural Networks and Reservoir Computing
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