| Literature DB >> 31559816 |
Yiyang Li1, Elliot J Fuller1, Shiva Asapu2, Sapan Agarwal1, Tomochika Kurita1,3, J Joshua Yang2, A Alec Talin1.
Abstract
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.Entities:
Keywords: TiO2; artificial synapse; diffusive memristor; electrochemical ion insertion; low-energy computing; redox transistor; subthreshold slope
Year: 2019 PMID: 31559816 DOI: 10.1021/acsami.9b14338
Source DB: PubMed Journal: ACS Appl Mater Interfaces ISSN: 1944-8244 Impact factor: 9.229