| Project/Area Number |
17K18881
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Electrical and electronic engineering and related fields
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| Research Institution | Osaka University |
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Principal Investigator |
Sakai Akira 大阪大学, 基礎工学研究科, 教授 (20314031)
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| Project Period (FY) |
2017-06-30 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥6,500,000 (Direct Cost: ¥5,000,000、Indirect Cost: ¥1,500,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2017: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
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| Keywords | シナプス素子 / メモリスタ / 酸素空孔分布 / トポロジー / 抵抗変化 / 電気着色現象 / 増強／抑圧特性 / ゲート効果 / 酸素空孔 / シナプス / ドリフト・拡散 / 有限要素法シミュレーション / 不可逆領域 / 走査透過電子顕微鏡 / 結晶場分裂 / 先端機能デバイス |
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| Outline of Final Research Achievements |
Resistive switching (RS) was demonstrated in four-terminal planar memristive devices fabricated on reduced TiO2 single crystal substrates. In the device, a pair of diagonally opposing electrode terminals (PADOET) is used to modify the topology of oxygen vacancy distribution in the region between another PADOET. The surface orientation of the substrate has a strong influence on the reversible RS phenomenon. Mechanisms behind the voltage-driven resistance change are elaborated for both crystalline and electronic structures in the electrically active zone of the device. Suppression of the irreversible conductive structure formation caused by accumulated oxygen vacancies is a key to establishing reversible RS in the device. Tuning protocols for the write and the gate voltage applications enables high precision control of resistance, or synaptic plasticity, paving the way for the manipulation of learning efficiency through neuromorphic devices.
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| Academic Significance and Societal Importance of the Research Achievements |
メモリスタ結晶における酸素空孔分布トポロジーをダイナミックに制御し，素子のバルク的電気抵抗に結晶学的異方性を持たせることは，物性改質の根源的理解にも通じ，その原子・電子構造と素子特性の相関に関わる物理学的知見を獲得することに学術的意義がある．また，本研究で開発する多元シナプス素子を用いれば，深層学習に関わる従来のソフトウェア的機械学習をハードウェア的に高密度に実装できる．加えて，一素子への連合的多入力によって，一シナプスの重みを他のシナプスとの相関性をもって遷移させる高次脳機能が発現されるため，学習や連想機能を模倣する次世代脳型コンピュータの発展に繋がり，社会的にも意義深い．
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