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

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 nonideal properties of synapses such as nonlinearity, asymmetry, limited bit precision, and dynamic weight update range within a constrained power budget. Herein, a fully scalable process is reported for digital and analog ferroelectric memory transistors with possibilities for both volatile and nonvolatile data retention and <4 V operation that would be suitable as programmable synaptic weight elements. Ferroelectric copolymer P(VDF-TrFE) gate insulator and 2D semiconductor MoS2 as the n-type semiconducting channel material make them suitable for flexible and wearable substrate integration. The ferroelectric-only devices show excellent performance as digital nonvolatile memory operating at <+/- 5 V while the hybrid ferroelectric-dielectric devices show quasi-continuous resistive switching resulting from gradual ferroelectric domain rotation. Analog conductance states of the hybrid devices allow good linearity and symmetry of weight updates and produce a dynamic conductance range of 104 with >16 reproducible conducting states. Network training experiments with these ferroelectric field-effect transistors show >96% classification accuracy with Modified National Institute of Standards and Technology (MNIST) handwritten datasets highlighting their potential for implementation in scaled DNN architectures.