Quantum Dot Light-Emitting Synaptic Transistor for Parallel Data Transmission of Diverse Artificial Neural Network

Artificial synaptic devices serve as the cornerstone of artificial neural networks, much research is devoted to the development of artificial synaptic devices with multiple functions for the future construction of large-scale artificial neural networks. By adding optical signal output to traditional synaptic devices, the strategy of transforming the devices from a single electrical interconnection to an optoelectronic interconnection is considered to be an effective way to solve the problem of wire cross-talk in large-scale artificial neural networks. Herein, a quantum-dot light-emitting synaptic transistor capable of dual output of optoelectronic signals by integrating the functions of light-emitting transistor and synaptic transistor into a single device is demonstrated for the first time. Based on the novel working mechanism and the excellent optoelectronic properties of quantum dots, the device can exhibit dual responses of electrical and optical signals under electrical pulse stimulation. More importantly, some key synaptic functions such as excitatory postsynaptic current, paired pulse facilitation, high-pass filtering properties, and the transition from short-term memory to long-term memory are successfully simulated in the device. In addition, classical conditioned reflex experiments as well as the processes of learning and forgetting are optically and electrically simulated. This work provides a feasible way to realize multivariate artificial neural networks with high integration and optoelectronic interconnection to transmit information, showing great potential in the development of neuromorphic computing in the future.

Automated summary

Artificial synaptic devices are important for artificial neural networks. In this study, a quantum-dot light-emitting synaptic transistor capable of dual output of optoelectronic signals is demonstrated. The device can exhibit dual responses of electrical and optical signals and successfully simulate key synaptic functions, showing potential for the development of neuromorphic computing.