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
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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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연구주제 1
Advanced Memory and Neural Computing