State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering Shanghai Jiao Tong University, Shanghai, 200240, China.
Key Laboratory of Thin Film and Microfabrication Technology (Ministry of Education), School of Electronics, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Nanoscale. 2022 Sep 15;14(35):12898-12908. doi: 10.1039/d2nr01996e.
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 AgNO 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 (∼10), more enriched conduction states (>2000), and much lower power consumption (∼3.6 μW) than previously reported biomaterial-based analog memristors. AgNO 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.
神经形态计算被认为是解决传统冯·诺依曼瓶颈的一种很有前途的方法。基于天然生物材料的人工突触是构建神经形态计算系统的常用单元，但存在线性度差和导通状态有限的问题。在这项工作中，提出了一种掺杂硝酸银的角叉菜聚糖（ι-卡拉胶）基忆阻器来解决非线性限制问题。与以前报道的基于生物材料的模拟忆阻器相比，该忆阻器具有线性可调电导、更高的耐久性（10 次）、更丰富的导通状态（>2000 种）和更低的功耗（3.6 μW）。将硝酸银掺杂到 ι-卡拉胶中，可以抑制 Ag 细丝的形成，从而消除不均匀的焦耳加热。将手写数字的深度学习作为一种应用，实现了增强的识别精度（93.8%），接近理想突触器件的识别精度（95.7%）。这项工作验证了使用生物聚合物进行未来高性能计算和可穿戴/可植入电子应用的可行性。
