期刊
ACS APPLIED MATERIALS & INTERFACES
卷 11, 期 42, 页码 38982-38992出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsami.9b14338
关键词
redox transistor; artificial synapse; low-energy computing; electrochemical ion insertion; subthreshold slope; TiO2; diffusive memristor
资金
- Laboratory-Directed Research and Development (LDRD) Programs: the Harry Truman Fellowship Program
- Hardware Acceleration of Adaptive Neural Algorithms (HAANA) Grand Challenge
- U.S. Department of Energy's National Nuclear Security Administration [DE-NA-0003525]
- Nanostructures for Electrical Energy Storage (NEES-II), an Energy Frontier Research Center - U.S. Department of Energy, Office of Science, Basic Energy Sciences [DESC0001160]
- U.S. Air Force Research Laboratory (AFRL) [12 FA8750-18-2-0122]
- Air Force Office of Scientific Research (AFOSR) [FA9550-19-1-0213]
Neuromorphic computers based on analogue neural networks aim to substantially lower computing power by reducing the need to shuttle data between memory and logic units. Artificial synapses containing nonvolatile analogue conductance states enable direct computation using memory elements; however, most nonvolatile analogue memories require high write voltages and large current densities and are accompanied by nonlinear and unpredictable weight updates. Here, we develop an inorganic redox transistor based on electrochemical lithium-ion insertion into LixTiO2 that displays linear weight updates at both low current densities and low write voltages. The write voltage, as low as 200 mV at room temperature, is achieved by minimizing the open-circuit voltage and using a low-voltage diffusive memristor selector. We further show that the LixTiO2 redox transistor can achieve an extremely sharp transistor subthreshold slope of just 40 mV/decade when operating in an electrochemically driven phase transformation regime.
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