Ga-Sn-O Thin Film Synapse for Neuromorphic Device

Shibayama, Yuki; Ohnishi, Yuki; Yamakawa, Daiki; Yamane, Hisanori; Nakashima, Yasuhiko; Kimura, Mutsumi

doi:10.23919/am-fpd49417.2020.9224491

Cited by 1 publication

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“…A voltage is applied to the bottom electrode and scanned to either + 3.5 V or − 3.5 V multiple times, and the electric current is measured. The characteristic uniformity of general AOS devices has been also investigated [21][22][23] .…”

Section: Methodsmentioning

confidence: 99%

Amorphous metal oxide semiconductor thin film, analog memristor, and autonomous local learning for neuromorphic systems

Kimura

Sumida

Kurasaki

et al. 2021

Sci Rep

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Artificial intelligence is a promising concept in modern and future societies. Presently, software programs are used but with a bulky computer size and large power consumption. Conversely, hardware systems named neuromorphic systems are suggested, with a compact computer size and low power consumption. An important factor is the number of processing elements that can be integrated. In the present study, three decisive technologies are proposed: (1) amorphous metal oxide semiconductor thin films, one of which, Ga–Sn–O (GTO) thin film, is used. GTO thin film does not contain rare metals and can be deposited by a simple process at room temperature. Here, oxygen-poor and oxygen-rich layers are stacked. GTO memristors are formed at cross points in a crossbar array; (2) analog memristor, in which, continuous and infinite information can be memorized in a single device. Here, the electrical conductance gradually changes when a voltage is applied to the GTO memristor. This is the effect of the drift and diffusion of the oxygen vacancies (Vo); and (3) autonomous local learning, i.e., extra control circuits are not required since a single device autonomously modifies its own electrical characteristic. Finally, a neuromorphic system is assembled using the abovementioned three technologies. The function of the letter recognition is confirmed, which can be regarded as an associative memory, a typical artificial intelligence application.

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Section: Methodsmentioning

confidence: 99%