Abstract
Ferroelectric field effect transistors (FeFETs) have attracted attention as next-generation devices as they can serve as a synaptic device for neuromorphic implementation and a one-transistor (1T) for achieving high integration. Since the discovery of hafnium–zirconium oxide (HZO) with high ferroelectricity (even at a thickness of several nanometers) that can be fabricated by a complementary metal–oxide–semiconductor-compatible process, FeFETs have emerged as devices with great potential. In this article, the basic principles of the FeFET and the design strategies for state-of-the-art FeFETs will be discussed. FeFETs using Pb(ZrxTi1−x)O3, polyvinylidene fluoride, HZO, and two-dimensional materials are emphasized. FeFETs, ferroelectric semiconductor field effect transistors, and metal–ferroelectric–insulator–semiconductor structures to which those materials can be applied are introduced, and their exotic performances are investigated. Finally, the limitations of these devices’ current performance and the potential of these materials are presented.
Highlights
The demand for machine learning and deep learning has increased as artificial intelligence (AI) technology is used throughout society
We reviewed the ferroelectricity of representative ferroelectric materials such as PZT, polyvinylidene fluoride (PVDF), hafnium–zirconium oxide (HZO), and 2D materials and the characteristics of field effect transistors (FeFETs) and ferroelectric semiconductor-field effect transistor (FeS-FET) devices
The polarization was varied according to external conditions, and this was mainly analyzed through piezoelectric force microscopy (PFM) and P–E curves
Summary
The demand for machine learning and deep learning has increased as artificial intelligence (AI) technology is used throughout society. Attempts have been made to break away from the existing von Neumann computer architecture, where the storage space and the computational processing space are separated.3–5 In response to these demands, ferroelectric field effect transistors (FeFETs) have received attention for next-generation FET devices because of fast operation speed, low power requirements, and non-destructive read capability as a memory device. A multi-level current flows between the source and the drain (Isd), and a synaptic weight (SW) can be formed by controlling the gate voltage pulse of the FeFET. It plays a critical role in neuromorphic computing [Figs. The research trends for the latest FeFETs will be reviewed for flexible devices, memory devices, and synapse devices, and the challenges that FeFETs must overcome in the future will be presented
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