Simplification of Processing Elements in Cellular Neural Networks

Kimura, Mutsumi; Nakamura, Nao; Yokoyama, Tomoharu; Matsuda, Tokiyoshi; Kameda, Tomoya; Nakashima, Yasuhiko

doi:10.1007/978-3-319-46672-9_35

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…Therefore, we are investigating "brain-type integrated system", namely, neural network built only by hardware, which can be compact, low power, robust, and integrated on everything in future. [5][6][7][8][9][10][11] In order to realize such system, simplification of the processing elements, such as neuron elements and synapse elements, three-dimensional structure, and low cost fabrication are required.…”

Section: Introductionmentioning

confidence: 99%

(Invited) Neuromorphic Application of Oxide Semiconductors

Kimura

Matsuda

Kameda³

et al. 2017

ECS Trans.

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Artificial intelligences are promising as key technologies in future societies, but the conventional ones are complicated software executed on high-specked hardware. Therefore, we are investigating "brain-type integrated system", namely, neural network built only by hardware, which can be compact, low power, robust, and integrated on everything. In this study, we have succeeded in hardware simplification and are trying to utilize oxide semiconductors for the neuromorphic application because they can be fabricated using low cost fabrication such as sputtering and printing. We made a sandwiched structure of Ti/a-IGZO/Ti and found that the conductance decreases as the time goes by, which is available to modified Hebbian rule, a learning rule we proposed for our network. We modeled the conductance degradation of the a-IGZO thin film, developed a simulator, and obtained the learning results. We would like to develop brain-type integrated system with three-dimensional structure utilizing the oxide semiconductors in future.

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