Ferroelectric Analog Synaptic Transistors for Neuromorphic Applications

Recently, neuromorphic hardware accelerators for image classification or speech recognition have attracted attention owing to their energy-efficient big-data processing capability compared to conventional computing hardware based on von-Neumann architecture. Emerging nonvolatile memory devices such as resistive switching memory, phase change memory, and ionic transistors are potential candidates for these hardware accelerators because of their analog conductance modulation characteristics. However, diverse requirements such as low cycle-to-cycle variation, high linearity/symmetry, and high endurance must still be fulfilled for realizing high-performance hardware accelerators. Here, we demonstrate ferroelectric thin-film transistors (FeTFTs) with nanoscale ferroelectric materials and oxide semiconductors. By using oxide semiconductor as the channel layer to create an interfacial layer-free metal-ferroelectric-semiconductor stack, our FeTFTs exhibit analog conductance modulation with exceptionally high endurance. Furthermore, linear and symmetric conductance modulation characteristics are achieved by precisely controlling the polarization state of the ferroelectric layer. Simulations performed based on measured properties show that a neuromorphic system with FeTFTs achieves high recognition accuracy for handwritten digits, which is close to the recognition accuracy achieved using ideal synapses. This study thus provides method of realizing neuromorphic hardware systems based on FeTFTs as synaptic devices.