TLDR:
Next-generation AI semiconductor devices have been developed by a team led by Prof. Kwon Hyuk-jun of DGIST. These devices mimic the human brain’s efficiency in AI and neuromorphic systems. The research focuses on synaptic field-effect transistors using hafnium oxide and tin disulfide, resulting in a highly efficient device.
Main Article:
A research team led by Prof. Kwon Hyuk-jun of the DGIST Department of Electrical Engineering and Computer Science has developed a next-generation AI semiconductor technology that mimics the human brain’s efficiency in AI and neuromorphic systems. The advancement of AI has stimulated a rapidly growing demand for energy-efficient semiconductor technology with a fast operational speed. Traditional computing devices have speed and energy efficiency shortcomings, leading to research on neuromorphic devices.
Prof. Hyuk-Jun Kwon’s team developed synaptic field-effect transistors using hafnium oxide and thin layers of tin disulfide, resulting in a three-terminal neuromorphic device capable of storing multiple levels of data. The research successfully replicated biological characteristics such as short- and long-term properties, yielding a device that responds 10,000 times faster than human synapses and consumes very little energy.
Prof. Hyuk-Jun Kwon stated, “This research marks an important step toward next-generation computing architecture, which requires low power consumption and high-speed computation. We have developed high-performance neuromorphic hardware using two-dimensional channels and ferroelectric hafnium oxide, with various potential AI and machine learning-related applications in the future.”
The research is published in the journal Advanced Science. This breakthrough in AI semiconductor technology opens up possibilities for more efficient and effective computing systems that mimic the human brain’s capabilities.