Synaptic plasticity and associative learning in IGZO-based synaptic transistor

Abstract

The increasing demand for efficient data access in high-performance computing systems has emphasized the limitations of the traditional von Neumann architecture, particularly due to the von Neumann bottleneck. This bottleneck arises from the significant power and time consumption required for data transfer between the central processing units and memory, impeding overall system efficiency. Neuromorphic computing offers a promising alternative. Neuromorphic systems emulate biological neurons and synapses, enabling parallel data processing and potentially overcoming the limitations of traditional computing architectures. This study focuses on synaptic transistors with In-Ga-Zn-O (IGZO) channels and TaOx charge trapping layers (Ox-CTL) to enhance neuromorphic computing capabilities. The devices were fabricated using reactive sputtering and atomic layer deposition, followed by comprehensive structural and compositional analyses. Experimental results demonstrated significant hysteresis, high on/off ratios, and multibit conductance states, suggestive of effective charge trapping and detrapping mechanisms. Evaluation through potentiation, depression, and excitatory postsynaptic current (EPSC) measurements, along with simulations using the Modified National Institute of Standards and Technology (MNIST) database, revealed improved pattern recognition accuracy. Additionally, associative learning experiments modeled after Pavlov's dog conditioning emphasized the device's capability for both long- and short-term memory retention. These findings suggest that IGZO-based synaptic transistors are promising candidates for nextgeneration neuromorphic computing systems, offering enhanced data processing efficiency and adaptability.