Ferroelectric Field Effect Transistors as a Synapse for Neuromorphic Application

Abstract

In spite of the increasing use of machine learning techniques, in-memory computing and hardware have increased the interest to accelerate neural network operation. Henceforth, novel embedded nonvolatile memories (eNVMs) for highly scaled technology nodes, like ferroelectric field effect transistors (FeFETs), are heavily studied and very promising. Furthermore, inference and on-chip learning can be fostered by further eNVM technology options, such as multibit operation and linear switching. In this article, we present the advantages of hafnium oxide-based FeFETs for such purposes due to their basic three-terminal structure, which allows to selectively activate or deactivate selected devices as well as tune linearity and dynamic range for certain applications. Furthermore, we discuss the impact of the material properties of the ferroelectric layer, the interface layer thickness, and scaling on the device performance. Here, we demonstrate good device properties even for highly scaled devices ( $100\,\,nm \times 100$ nm).