Enhancing memory window efficiency of ferroelectric transistor for neuromorphic computing via two-dimensional materials integration

Abstract

In-memory computing, particularly neuromorphic computing, has emerged as a promising solution to overcome the energy and time-consuming challenges associated with the von Neumann architecture. The ferroelectric field-effect transistor (FeFET) technology, with its fast and energy-efficient switching and nonvolatile memory, is a potential candidate for enabling both computing and memory within a single transistor. In this study, the capabilities of an integrated ferroelectric HfO2 and 2D MoS2 channel FeFET in achieving high-performance 4-bit per cell memory with low variation and power consumption synapses, while retaining the ability to implement diverse learning rules, are demonstrated. Notably, this device accurately recognizes MNIST handwritten digits with over 94% accuracy using online training mode. These results highlight the potential of FeFET-based in-memory computing for future neuromorphic computing applications.