1. Dielectric Engineered Two-Dimensional Neuromorphic Transistors
- Author
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Wei Chen, Du Xiang, Peng Zhou, Xumeng Zhang, and Tao Liu
- Subjects
Computer science ,Bioengineering ,02 engineering and technology ,Substrate (electronics) ,Dielectric ,law.invention ,chemistry.chemical_compound ,law ,Electronic engineering ,General Materials Science ,Basis (linear algebra) ,Mechanical Engineering ,Transistor ,Oxides ,General Chemistry ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,Semiconductors ,Silicon nitride ,chemistry ,Neuromorphic engineering ,Synapses ,Scalability ,Neural Networks, Computer ,0210 nano-technology ,MNIST database - Abstract
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.
- Published
- 2021