Perspective on oxide-based three-terminal artificial synapses in physical neural networks

• Published in: Applied physics letters Vol. 121; no. 19
• Main Authors: Chen, Kuan-Ting, Chen, Jen-Sue
• Format: Journal Article
• Language: English
• Published: Melville American Institute of Physics 07.11.2022
• Subjects: Applied physics; Artificial neural networks; CMOS; Data processing; Electrolytes; Ferroelectricity; Hardware; Neural networks; Neuromorphic computing; Power consumption; Relocation; Semiconductor devices; Synapses; Transistors; Unstructured data
• ISSN: 0003-6951; 1077-3118
• DOI: 10.1063/5.0115449

The physical implementation of artificial neural networks, also known as “neuromorphic engineering” as advocated by Carver Mead in the late 1980s, has become urgent because of the increasing demand on massive and unstructured data processing. complementary metal-oxide-semiconductor-based hardware suffers from high power consumption due to the von Neumann bottleneck; therefore, alternative hardware architectures and devices meeting the energy efficiency requirements are being extensively investigated for neuromorphic computing. Among the emerging neuromorphic electronics, oxide-based three-terminal artificial synapses merit the features of scalability and compatibility with the silicon technology as well as the concurrent signal transmitting-and-learning. In this Perspective, we survey four types of three-terminal artificial synapses classified by their operation mechanisms, including the oxide electrolyte-gated transistor, ion-doped oxide electrolyte-gated transistor, ferroelectric-gated transistor, and charge trapping-gated transistor. The synaptic functions mimicked by these devices are analyzed based on the tunability of the channel conductance correlated with the charge relocation and polarization in gate dielectrics. Finally, the opportunities and challenges of implementing oxide-based three-terminal artificial synapses in physical neural networks are delineated for future prospects.