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Dielectric Engineered Two-Dimensional Neuromorphic Transistors
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DOI：10.1021/acs.nanolett.1c00492.png)
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Abstract
En 中文
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-SiNx) 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-SiNx dielectric, promoting a brand-new paradigm of 2D materials in neuromorphic computing.
Keywords:
silicon-rich silicon nitride substrate
analogue nonvolatile memory
TMDC-based synaptic array
low cycle-to-cycle and device-to-device variability
ANN simulation
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