Back-end and Flexible Substrate Compatible Analog Ferroelectric Field Effect Transistors for Accurate Online Training in Deep Neural Network Accelerators

Abstract

Online training of deep neural networks (DNN) can be significantly accelerated by performing in-situ vector matrix multiplication in a crossbar array of analog memories. However, training accuracies often suffer due to device non-idealities such as nonlinearity, asymmetry, limited bit precision and dynamic weight update range within constrained power budget. Here, we report a three-terminal Ferroelectric-Field-Effect-Transistor based on low thermal budget processes that can work efficiently as an analog synaptic transistor. Ferroelectric polymer P(VDF-TrFE) as the gate insulator and 2D semiconductor MoS2 as the n-type semiconducting channel material makes them suitable for flexible and wearable substrate integration. The analog conductance of the FeFETs can be precisely manipulated by employing a ferroelectric-dielectric layer as the gate stack. The ferroelectric-only devices show excellent performance as digital non-volatile memory operating at +-5V while the hybrid ferroelectric-dielectric devices show quasi-continuous resistive switching resulting from gradual ferroelectric domain rotation, important for their multibit operation. Analog conductance states of the hybrid devices allow linearity and symmetry of weight updates and produce a dynamic conductance range of 104 with >16 reproducible conducting states. Network training experiments of these FeFETs show >96% classification accuracy with MNIST handwritten datasets highlighting their potential for implementation in scaled DNN architectures.