The use of very-large-scale integration (VLSI) systems with electronic analog circuits to replicate the neuro-biological architectures seen in the nervous system is known as neuromorphic engineering, also referred to as neuromorphic computing. Any device that does computations using silicon-based physical artificial neurons is referred to as a neuromorphic computer or chip. In recent years, analog, digital, mixed-mode analog/digital VLSI, and software systems that incorporate neural system models have all been referred to as neuromorphic. Spintronic memories, transistors, threshold switches, and oxide-based memristors can all be used to build neuromorphic computing at the hardware level.
Researchers have reported a nano-sized neuromorphic memory device that emulates neurons and synapses in a unit cell at the same time, which is another step toward the goal of neuromorphic computing, which is designed to rigorously mimic the human brain with semiconductor devices.
Neuromorphic computing aims to realize artificial intelligence (AI) by mimicking the mechanisms of neurons and synapses that make up the human brain. Inspired by the cognitive functions of the human brain that current computers cannot provide, neuromorphic devices have been widely investigated. However, current Complementary Metal-Oxide Semiconductor (CMOS)-based neuromorphic circuits simply connect artificial neurons and synapses without synergistic interactions, and the concomitant implementation of neurons and synapses still remains a challenge.
Because neurons and synapses interact to establish cognitive functions such as memory and learning, simulating both is an essential component of brain-inspired artificial intelligence. By implementing a positive feedback effect between neurons and synapses, the developed neuromorphic memory device also mimics the retraining effect, which allows for quick learning of forgotten information.Professor Keon Jae Lee
To address these issues, a research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering implemented the biological working mechanisms of humans by introducing the neuron-synapse interactions in a single memory cell, rather than the conventional approach of electrically connecting artificial neuronal and synaptic devices.
Similar to commercial graphics cards, the artificial synaptic devices previously studied are often used to accelerate parallel computations, which shows clear differences from the operational mechanisms of the human brain. The research team implemented the synergistic interactions between neurons and synapses in the neuromorphic memory device, emulating the mechanisms of the biological neural network. In addition, the developed neuromorphic device can replace complex CMOS neuron circuits with a single device, providing high scalability and cost efficiency.
The human brain consists of a complex network of 100 billion neurons and 100 trillion synapses. The functions and structures of neurons and synapses can flexibly change according to external stimuli, adapting to the surrounding environment. The research team developed a neuromorphic device in which short-term and long-term memories coexist using volatile and non-volatile memory devices that mimic the characteristics of neurons and synapses, respectively.
A threshold switch device is used as volatile memory and phase-change memory is used as a non-volatile device. Two thin-film devices are integrated without intermediate electrodes, implementing the functional adaptability of neurons and synapses in the neuromorphic memory.
Professor Keon Jae Lee explained, “Because neurons and synapses interact to establish cognitive functions such as memory and learning, simulating both is an essential component of brain-inspired artificial intelligence. By implementing a positive feedback effect between neurons and synapses, the developed neuromorphic memory device also mimics the retraining effect, which allows for quick learning of forgotten information.”