SNUEI

Wafer-Scale All-Silicon Self-Rectifying Memristor for Synaptic Response and Reservoir Computing

2026 · Nano Letters

저자

Jeong Hyun Yoon, Sung Hoon Cho, Peter Moroshkin, Sungho Kim, Manuel Dal Bo, Jang‐Yeon Kwon, 남기태 (Ki Tae Nam), Jimmy Xu

연구주제

Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices

NTIS 과학기술표준분류

에너지/자원만에너지·환경·원자력 확장

에너지/자원 > 신재생에너지 > 태양광에너지 외

배정 근거: AI 키워드가 잡은 NTIS 과제의 분류(정책 연계 맵과 같은 방법).

AI 연구 키워드·방법

리저버 컴퓨팅 self rectifying memristor silicon p n junction

키워드 (OpenAlex)

memristors silicon p-n junctions reservoir computing synaptic plasticity charge trapping paired-pulse facilitation spike-timing-dependent plasticity long-term memory

초록

Silicon p-n junctions have remained an indispensable building block of electronics since their invention in the Shockley days. Likewise, an abrupt p-n junction has served as a foundational model in semiconductor textbooks. In this work, we report on an p-n junction in silicon with an oxide interfacial layer, enabling memristive functions with highly rectifying resistive switching and reproducible synaptic behaviors for reservoir computing (RC). The device exhibited a rectification ratio of ∼5000 with stable endurance of 4.5 × 106 cycles, without filament formation. Charge-trapping dynamics enable key synaptic behaviors including paired-pulse facilitation, spike-timing-dependent plasticity, and transitions between short- and long-term memory. Leveraging these behaviors, the device performs RC via 4-bit pulse stimulation, achieving 86.9% accuracy in handwritten digit classification. This i…

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NTIS 성과DB(2020–24)에서 DOI 매칭 없음

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교수 1

남기태

연구주제 1

Advanced Memory and Neural Computing

정책분야 1

신재생에너지

키워드 1

리저버 컴퓨팅