期刊
SCIENCE ADVANCES
卷 7, 期 32, 页码 -出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.abg8836
关键词
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资金
- National Research Foundation (NRF) of Korea [2018R1A2A3075302, 2019M3F3A1A03079603, 2017R1A2B3007806]
- IC Design Education Center (EDA Tool)
- Samsung Electronics Co. Ltd [IO201210-08017-01]
- IC Design Education Center (MPW)
- National Research Foundation of Korea [2017R1A2B3007806] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The study demonstrates the cointegration of multistate single-transistor neurons and synapses on highly scalable neuromorphic hardware, achieved through nanoscale CMOS fabrication. This integration allows for enhanced packing density, reduced chip cost, and simplified fabrication procedures. The fabricated single-transistor neurons and synapses exhibit spatiotemporal neuronal functionalities, with applications in image processing for pattern recognition and face image recognition.
Cointegration of multistate single-transistor neurons and synapses was demonstrated for highly scalable neuromorphic hardware, using nanoscale complementary metal-oxide semiconductor (CMOS) fabrication. The neurons and synapses were integrated on the same plane with the same process because they have the same structure of a metal-oxide semiconductor field-effect transistor with different functions such as homotype. By virtue of 100% CMOS compatibility, it was also realized to cointegrate the neurons and synapses with additional CMOS circuits. Such cointegration can enhance packing density, reduce chip cost, and simplify fabrication procedures. The multistate single-transistor neuron that can control neuronal inhibition and the firing threshold voltage was achieved for an energy-efficient and reliable neural network. Spatiotemporal neuronal functionalities are demonstrated with fabricated single-transistor neurons and synapses. Image processing for letter pattern recognition and face image recognition is performed using experimental-based neuromorphic simulation.
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