Highly-scaled and fully-integrated 3-dimensional ferroelectric transistor array for hardware implementation of neural networks.

ABSTRACT: Hardware-based neural networks (NNs) can provide a significant breakthrough in artificial intelligence applications due to their ability to extract features from unstructured data and learn from them. However, realizing complex NN models remains challenging because different tasks, such as feature extraction and classification, should be performed at different memory elements and arrays. This further increases the required number of memory arrays and chip size. Here, we propose a three-dimensional ferroelectric NAND (3D FeNAND) array for the area-efficient hardware implementation of NNs. Vector-matrix multiplication is successfully demonstrated using the integrated 3D FeNAND arrays, and excellent pattern classification is achieved. By allocating each array of vertical layers in 3D FeNAND a

SUBMITTER: Kim IJ 

PROVIDER: S-EPMC9889761 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature