4.8 Article

Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse

Journal

ACS APPLIED MATERIALS & INTERFACES
Volume 15, Issue 14, Pages 18055-18064

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsami.2c23218

Keywords

perovskites; quantum dots; CsPbBr3; charge trapping; nonvolatile memories; artificial synapses; neuromorphic computing

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This study demonstrates a photonic multimodal synaptic device with favorable band alignment that enables optically induced charge trapping and nonvolatile memory characteristics. The device exhibits high photoresponse and excellent synaptic characteristics through gate voltage regulation. It also shows multiwavelength response and a large dynamic range suitable for accurate artificial neural network. The simulation results based on experimental data show excellent pattern recognition accuracy after 120 epochs, demonstrating the feasibility of the device as an optical synapse in the next-generation neuromorphic system.
Artificial synapse is the basic unit of a neuromorphic computing system. However, there is a need to explore suitable synaptic devices for the emulation of synaptic dynamics. This study demonstrates a photonic multimodal synaptic device by implementing a perovskite quantum dot charge-trapping layer in the organic poly(3-hexylthiophene-2,5-diyl) (P3HT) channel transistor. The proposed device presents favorable band alignment that facilitates spatial separation of photogenerated charge carriers. The band alignment serves as the basis of optically induced charge trapping, which enables nonvolatile memory characteristics in the device. Furthermore, high photoresponse and excellent synaptic characteristics, such as short-term plasticity, long-term plasticity, excitatory postsynaptic current, and paired-pulse facilitation, are obtained through gate voltage regulation. Photosynaptic characteristics obtained from the device showed a multiwavelength response and a large dynamic range (similar to 103) that is suitable for realizing a highly accurate artificial neural network. Moreover, the device showed nearly linear synaptic weight update characteristics with incremental depression electric gate pulse. The simulation based on the experimental data showed excellent pattern recognition accuracy (similar to 85%) after 120 epochs. The results of this study demonstrate the feasibility of the device as an optical synapse in the next-generation neuromorphic system.

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