Ferroelectric Field-Effect Transistors for Binary Neural Network With 3-D NAND Architecture

Ferroelectric field-effect transistor (FeFET) can be operated as a nonvolatile memory device with low programming voltage based on polarization. In particular, it can be used as a synaptic device in a neuromorphic system based on the NAND flash array structure. We demonstrate a Hf0.5Zr0.5O2 (HZO)-based FeFET device fabricated on a silicon-on-insulator (SOI) substrate with high ON/OFF ratio and reliability characteristics. The HZO-based FeFET is utilized as a synaptic device based on the 3-D NAND architecture. It is verified with the binarization of input–output signals and weight value for efficient vector–matrix multiplication (VMM) operation using the 3-D NAND architecture. In addition, a neural network layer-mapping method increasing synaptic cell efficiency is proposed. A system-level simulation is performed based on the FeFET single-device experimental data. The VMM operation is verified through the SPICE Berkeley short-channel IGFET model (BSIM), and off-chip (ex-situ) learning with binary neural network (BNN) is performed for the Modified National Institute of Standards and Technology Database MNIST and fashion-MNIST data. The results confirm that the proposed FeFET-based BNN can perform accurate VMM operations and is robust to variations due to the binary weight state.