神经形态工程学
记忆电阻器
电导
瓶颈
功率(物理)
材料科学
人工神经网络
兴奋剂
热传导
线性
计算机科学
纳米技术
冯·诺依曼建筑
焦耳加热
人工智能
电子工程
光电子学
工程类
物理
嵌入式系统
凝聚态物理
复合材料
量子力学
操作系统
作者
Ke Zhang,Qi Xue,Chao Zhou,Wanneng Mo,Chun‐Chao Chen,Ming Li,Tao Hang
出处
期刊:Nanoscale
[The Royal Society of Chemistry]
日期:2022-01-01
卷期号:14 (35): 12898-12908
被引量:7
摘要
Neuromorphic computing is considered a promising method for resolving the traditional von Neumann bottleneck. Natural biomaterial-based artificial synapses are popular units for constructing neuromorphic computing systems while suffering from poor linearity and limited conduction states. In this work, a AgNO3 doped iota-carrageenan (ι-car) based memristor is proposed to resolve the non-linear limitation. The memristor presents linear conductance tuning with a higher endurance (∼104), more enriched conduction states (>2000), and much lower power consumption (∼3.6 μW) than previously reported biomaterial-based analog memristors. AgNO3 is doped to ι-car to suppress the formation of Ag filaments, thereby eliminating uneven Joule heating. Using deep learning of hand-written digits as an application, a doping-enhanced recognition accuracy (93.8%) is achieved, close to that of an ideal synaptic device (95.7%). This work verifies the feasibility of using biopolymers for future high-performance computational and wearable/implantable electronic applications.
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