BEOL-Compatible Superlattice FEFET Analog Synapse With Improved Linearity and Symmetry of Weight Update

Pseudo-crossbar arrays using ferroelectric field effect transistor (FEFET) mitigates weight movement and allows in situ vector–matrix multiplication (VMM), which can significantly accelerate online training of deep neural networks (DNNs). However, the training accuracy of DNNs using conventional FEFETs is low because of the non-idealities, such as nonlinearity, asymmetry, limited bit precision, and limited dynamic range of the weight updates. The limited endurance of these devices degrades the training accuracy further. Here, we show a novel approach for designing the gate-stack of an FEFET analog synapse using a superlattice (SL) of ferroelectric (FE) /dielectric (DE) /FE. The partial polarization states are stabilized by harnessing the depolarization field from the DE spacer, which mitigates the weight update non-idealities. We demonstrate a 7-bit SL-FEFET analog synapse with improved weight update profile, resulting in 94. 1% online training accuracy for MNIST handwritten digit classification task. The device uses an indium–tungsten–oxide (IWO) channel and back-end-of line (BEOL) -compatible process flow. The absence of low- k interlayer (IL) results in high endurance (>1010 cycles), while the BEOL compatibility paves the way to high-density integration of pseudo-crossbar arrays and flexibility for neuromorphic circuit design.