Abstract
In order to resolve the issue of tremendous energy consumption in conventional artificial intelligence, hardware-based neuromorphic system is being actively studied. Although various synaptic devices for the system have been proposed, they have shown limits in terms of endurance, reliability, energy efficiency, and Si processing compatibility. In this work, we design a synaptic transistor with short-term and long-term plasticity, high density, high reliability and energy efficiency, and Si processing compatibility. The synaptic characteristics of the device are closely examined and validated through technology computer-aided design (TCAD) device simulation. Consequently, full synaptic functions with high energy efficiency have been realized.
Highlights
Conventional computer architectures are mostly based on von Neumann’s architecture since modern computer systems have been represented by electronic delay storage automatic calculator (EDSAC)—since 1949
Unimaginably many kinds of tasks have been accomplished by the software-driven technology in the given hardware system, with great resemblance to the way the human brain works, there is much room for enhancement of energy efficiency, which is the incomparable essence of biological system
As a solution for the energy consumption issue, spiking neural network (SNN) is considered as one of the powerful schemes inspired by the biological system, which requires fundamental hardware innovation with synaptic transistors and neuron circuits [1,2]
Summary
Conventional computer architectures are mostly based on von Neumann’s architecture since modern computer systems have been represented by electronic delay storage automatic calculator (EDSAC)—since 1949. In order to overcome this limit in the von Neumann computer architecture parallel processing capability of the artificial intelligent, of parallel processing with tremendous amount of data, contributions have been dedicated by the software-based neural networks. Unimaginably many kinds of tasks have been accomplished by the software-driven technology in the given hardware system, with great resemblance to the way the human brain works, there is much room for enhancement of energy efficiency, which is the incomparable essence of biological system. As a solution for the energy consumption issue, spiking neural network (SNN) is considered as one of the powerful schemes inspired by the biological system, which requires fundamental hardware innovation with synaptic transistors and neuron circuits [1,2]. For hardware-driven neuromorphic systems to achieve more human-brain like computing efficiency, the synaptic device is required to have high
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