Oxide semiconductor-based ferroelectric thin-film transistors for advanced neuromorphic computing

Abstract

Neuromorphic computing that mimics the biological brain has been demonstrated as a next-generation computing method due to its low power consumption and parallel data processing characteristics. To realize neuromorphic computing, diverse neural networks such as deep neural networks (DNNs) and spiking neural networks (SNNs) have been introduced. DNNs require artificial synapses that have analog conductance modulation characteristics, whereas SNNs require artificial synapses that have conductance modulation characteristics controlled by temporal relationships between signals, so the development of a multifunctional artificial synapse is required. In this work, we report a ferroelectric thin-film transistor (FeTFT) that uses zirconium-doped hafnia (HfZrOx) and indium zinc tin oxide (IZTO) for neuromorphic applications. With reliable conductance modulation characteristics, we suggest that the FeTFT with HfZrOx and IZTO can be used as an artificial synapse for both DNNs and SNNs. The linear and symmetric conductance modulation characteristics in FeTFTs result in high recognition accuracy (93.1%) of hand-written images, which is close to the accuracy (94.1%) of an ideal neural network. Also, we show that the FeTFTs can emulate diverse forms of spike-time-dependent plasticity, which is an important learning rule for SNNs. These results suggest that FeTFT is a promising candidate to realize neuromorphic computing hardware.