Influence of characteristic variation of oxide semiconductor and comparison of the activation function in neuromorphic hardware

Ikeda, Hiroya; Yamane, Hisanori; Takishita, Yuta; Kimura, Mutsumi; Nakashima, Yasuhiko

doi:10.1587/nolta.11.232

Cited by 5 publications

(3 citation statements)

References 29 publications

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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%

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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“…7 We are researching neuromorphic systems. To date, we have reported the research results of actual experiments on amorphous-metal-oxide semiconductor (AOS) thin-film binary and analog memristors, [8][9][10][11] AOS thin-film spiketiming-dependent-plasticity (STDP) devices, 12,13 and a neuromorphic system equipped with crossbar-type AOS thin-film synapses, 14 and the simulation results on a neuromorphic chip integrated with an LSI and AOS thinfilm synapse devices, 15 together with local autonomous learning. 16 In this research, as a culmination of the above research results, we have developed a neuromorphic chip integrated with an LSI and amorphous-metal-oxide semiconductor (AOS) thin-film synapse devices utilizing local autonomous learning.…”

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confidence: 99%

Neuromorphic Chip Integrated with an LSI and Amorphous-metal-oxide Semiconductor Thin-film Synapse Devices

Kimura

Shibayama

Nakashima

2022

Preprint

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Artificial intelligences are promising in future societies, and neural networks are typical technologies with the advantages such as self-organization, self-learning, parallel distributed computing, and fault tolerance, but their size and power consumption are large. Neuromorphic systems are biomimetic systems from the hardware level, with the same advantages as living brains, especially compact size, low power, and robust operation, but some well-known ones are non-optimized systems, so the above benefits are only partially gained, for example, machine learning is processed elsewhere to download fixed parameters. To solve these problems, we are researching neuromorphic systems from various viewpoints. In this study, a neuromorphic chip integrated with an LSI and amorphous-metal-oxide semiconductor (AOS) thin-film synapse devices has been developed. The neuron elements are digital circuit, which are made in an LSI, and the synapse devices are analog devices, which are made of the AOS thin film and directly integrated on the LSI. This is the world's first hybrid chip where neuron elements and synapse devices of different functional semiconductors are integrated, and local autonomous learning is utilized, which becomes possible because the AOS thin film can be deposited without heat treatment and there is no damage to the underneath layer, and has all advantages of neuromorphic systems.

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“…30) Therefore, it is believed that they are some of the most suitable materials for neuromorphic systems, which require distinctive properties and astronomically high integration. 31,32) In particular, we are investigating amorphous Ga-Sn-O (α-GTO) thin films, which do not contain In, a rare metal, 33,34) also for neuromorphic systems, 35,36) which require memristive devices [37][38][39][40][41][42][43][44][45] and STDP devices. 46,47) In this study, a switchover behavior between long-term potentiation (LTP) characteristic and long-term depression (LTD) characteristic in an α-GTO thin-film STDP device has been observed.…”

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confidence: 99%

Switchover behavior between long-term potentiation and depression in amorphous Ga–Sn–O thin-film spike-timing-dependent-plasticity device

Morigaki¹,

Yatida²,

Katagiri³

et al. 2022

Jpn. J. Appl. Phys.

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A switchover behavior between long-term potentiation (LTP) and long-term depression (LTD) characteristics in an amorphous Ga-Sn-O (a-GTO) thin-film spike-timing-dependent-plasticity (STDP) device has been observed. The LTP characteristic is induced by applying short-width pulses, whereas the LTP and LTD hybrid characteristic is induced by applying long-width pulses. This is suggested to be due to the difference between drift velocities of oxygen ions and vacancies. The switchover behavior may be useful for general operations of neuromorphic systems.

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Influence of characteristic variation of oxide semiconductor and comparison of the activation function in neuromorphic hardware

Cited by 5 publications

References 29 publications

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

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

Neuromorphic Chip Integrated with an LSI and Amorphous-metal-oxide Semiconductor Thin-film Synapse Devices

Switchover behavior between long-term potentiation and depression in amorphous Ga–Sn–O thin-film spike-timing-dependent-plasticity device

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