2022 International Conference on Solid State Devices and Materials

講演情報

Oral Presentation

Focus Session 2 (Area1&2&8)

[F-10] CMOS and Memory Applications of Low Dimensional Materials II

2022年9月29日(木) 16:00 〜 17:45 201 (2F)

Session Chair: Takamasa Kawanago (Tokyo Tech), Sakura Takeda (NAIST)

16:30 〜 16:45

[F-10-02] Neural Network Hardware Accelerator using Memristive Crossbar Array based on Wafer-Scale 2D HfSe2

〇Sifan Li1, Samarth Jain1, Mei-Er Pam1, Li Chen1, Yu-Chieh Chien1, Xuanyao Fong1, Dongzhi Chi2, Kah-Wee Ang1,2 (1. National Univ. of Singapore (Singapore), 2. Inst. of Material Res. and Engineering (Singapore))

Presentation style: On-site (in-person)

https://doi.org/10.7567/SSDM.2022.F-10-02

For the first time, hardware acceleration of neural network calculations is demonstrated using large memristive crossbar arrays (CBAs) based on two-dimensional (2D) hafnium diselenide (HfSe2). Growth of wafer-scale polycrystalline HfSe2 thin film and metal-assisted van der Waals (vdW) transfer method are developed. The memristor exhibits a small switching voltage (0.6 V) with low switching energy (0.82 pJ) and achieves synaptic long-term potentiation/depression (LTP/D) with high offline classification accuracy (93.34%) in modeled artificial neural network (ANN). Furthermore, the CBA integrates custom-designed periphery circuits to implement full-hardware convolutional image processing, achieving large-scale image edge detection, soft and inverse functions with a tight current distribution of σ = 0.25 %. This work opens a potential route to the development of large-scale and energy-efficient neuromorphic computing systems.

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