Memcapacitor Crossbar Array with Charge Trap NAND Flash Structure for Neuromorphic Computing

초록

The progress of artificial intelligence and the development of large-scale neural networks have significantly increased computational costs and energy consumption. To address these challenges, researchers are exploring low-power neural network implementation approaches and neuromorphic computing systems are being highlighted as potential candidates. Specifically, the development of high-density and reliable synaptic devices, which are the key elements of neuromorphic systems, is of particular interest. In this study, an 8 x 16 memcapacitor crossbar array that combines the technological maturity of flash cells with the advantages of NAND flash array structure is presented. The analog properties of the array with high reliability are experimentally demonstrated, and vector-matrix multiplication with extremely low error is successfully performed. Additionally, with the capability of weight fine-tuning characteristics, a spiking neural network for CIFAR-10 classification via off-chip learning at the wafer level is implemented. These experimental results demonstrate a high level of accuracy of 92.11%, with less than a 1.13% difference compared to software-based neural networks (93.24%). Memcapacitor crossbar array based on NAND flash array structure is demonstrated for neuromorphic computing. A MOS capacitor is utilized as a memcapacitor with a charge-trapping layer. Memcapacitive array operations are experimentally verified including vector-matrix multiplication, and hardware SNN is demonstrated based on VGGNet-7 for CIFAR-10 classification.image

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