Analog Synaptic Transistor with Al-Doped HfO2 Ferroelectric Thin Film

Neuromorphic computing has garnered significant attention because it can overcome the limitations of the current von-Neumann computing system. Analog synaptic devices are essential for realizing hardware-based artificial neuromorphic devices; however, only a few systematic studies in terms of both synaptic materials and device structures have been conducted so far, and thus, further research is required in this direction. In this study, we demonstrate the synaptic characteristics of a ferroelectric material-based thin-film transistor (FeTFT) that uses partial switching of ferroelectric polarization to implement analog conductance modulation. For a ferroelectric material, an aluminum-doped hafnium oxide (Al-doped HfO2) thin film was prepared by atomic layer deposition. As an analog synaptic device, our FeTFT successfully emulated short-term plasticity and long-term plasticity characteristics, such as paired-pulse facilitation and spike timing-dependent plasticity. In addition, we obtained potentiation/depression weight updates with high linearity, an on/off ratio, and low cycle-to-cycle variation by adjusting the amplitude and number of input pulses. In the simulation trained with optimized potentiation/depression conditions, we achieved a pattern recognition accuracy of approximately 90% for the Modified National Institute of Standard and Technology (MNIST) handwritten data set. Our results indicated that ferroelectric transistors can be used as an alternative artificial synapse.

Automated summary

Neuromorphic computing has attracted attention for overcoming the limitations of von-Neumann computing, with analog synaptic devices playing a crucial role in hardware-based artificial neuromorphic devices. This study demonstrates the synaptic characteristics of a ferroelectric material-based thin-film transistor, showing successful emulation of short-term and long-term plasticity. The research suggests that ferroelectric transistors can serve as alternative artificial synapses with high linearity and pattern recognition accuracy.