Frontier Institute of Chip and System, Fudan University, Shanghai 200438, China.
Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China.
Nano Lett. 2021 Apr 28;21(8):3557-3565. doi: 10.1021/acs.nanolett.1c00492. Epub 2021 Apr 9.
Two-dimensional (2D) materials, which exhibit planar-wafer technique compatibility and pure electrically triggered communication, have established themselves as potential candidates in neuromorphic architecture integration. However, the current 2D artificial synapses are mainly realized at a single-device level, where the development of 2D scalable synaptic arrays with complementary metal-oxide-semiconductor compatibility remains challenging. Here, we report a 2D transition metal dichalcogenide-based synaptic array fabricated on commercial silicon-rich silicon nitride (sr-SiN) substrate. The array demonstrates uniform performance with sufficiently high analogue on/off ratio and linear conductance update, and low cycle-to-cycle variability (1.5%) and device-to-device variability (5.3%), which are essential for neuromorphic hardware implementation. On the basis of the experimental data, we further prove that the artificial synapses can achieve a recognition accuracy of 91% on the MNIST handwritten data set. Our findings offer a simple approach to achieve 2D synaptic arrays by using an industry-compatible sr-SiN dielectric, promoting a brand-new paradigm of 2D materials in neuromorphic computing.
二维（2D）材料具有平面晶圆技术兼容性和纯电触发通信，已成为神经形态架构集成的潜在候选材料。然而，目前的 2D 人工突触主要在单个器件层面实现，而具有互补金属氧化物半导体兼容性的 2D 可扩展突触阵列的开发仍然具有挑战性。在这里，我们报告了一种在商业富硅氮化硅（sr-SiN）衬底上制造的基于二维过渡金属二卤化物的突触阵列。该阵列表现出均匀的性能，具有足够高的模拟导通/截止比和线性电导更新，以及低的循环间变化率（1.5%）和器件间变化率（5.3%），这对于神经形态硬件实现至关重要。基于实验数据，我们进一步证明人工突触可以在手写数字 MNIST 数据集上实现 91%的识别准确率。我们的研究结果提供了一种简单的方法，可以使用与工业兼容的 sr-SiN 电介质来实现 2D 突触阵列，推动了 2D 材料在神经形态计算中的全新范例。
