表面と真空
Online ISSN : 2433-5843
Print ISSN : 2433-5835
特集「物理リザバー・コンピューティング」
導電性高分子ネットワークを用いたマテリアルリザバー演算
宇佐美 雄生 琴岡 匠松本 卓也田中 啓文
著者情報
ジャーナル 認証あり

2024 年 67 巻 11 号 p. 527-532

詳細
抄録

Artificial intelligence (AI) has rapidly advanced and is being utilized across a wide range of fields by developing artificial neural network (ANN). However, constructing large-scale ANNs requires significant power consumption. In the field of materials and device engineering, “physical reservoir computing (physical RC)” is gaining attention to perform ANN computations with low power consumption. Unlike traditional ANNs, physical RC leverages physical properties for computation, resulting in energy efficiency. Conductive polymers such as polyaniline are expected to exhibit excellent performance in in-materio physical reservoir computing (IMRC) due to their superior environmental stability and reversible doping behavior. This paper introduces about utilizing the mixed polaron-ion conductivity of sulfonated polyaniline (SPAN), evaluating changes in electrical properties with humidity adjustments, and exploring its potential application in IMRC by investigating the correlation between the electrical properties and computational ability.

Fullsize Image
著者関連情報

この記事はクリエイティブ・コモンズ [表示 - 非営利 4.0 国際]ライセンスの下に提供されています。
https://creativecommons.org/licenses/by-nc/4.0/deed.ja
前の記事 次の記事
feedback
Top