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Updated: Jun 4, 2025
Automatic Identification of Dendritic Branches and their Orientation
Published on: September 17, 2021
Highly Reliable Bi2O2Se Dendritic Neuron Enabling Spatial-Temporal Signal Processing for Real-World Image
Jungyeop Oh1, Wonbae Ahn1, Ayoung Ham2
1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
Abstract:
Artificial intelligence (AI) has made significant strides by imitating biological neurons and synapses through simplified models, yet incomplete neuron functionalities can limit performance and energy efficiency in handling complex tasks. Biological neurons process input signals nonlinearly, utilizing dendrites to process spatial-temporal information. This study demonstrates the compact artificial dendrite device employing memristors based on bismuth oxyselenide (Bi2O2Se). Transfer-free Bi2O2Se switching medium is directly grown on the metal-patterned substrates via 350 °C selenization process. The layered Bi2O2Se structure, limiting metal injection, results in reliable dynamic resistive switching with excellent cycle uniformity and exceptional endurance over 2 million cycles. The highly reliable current response of dynamic resistive switching is modeled with respect to the spatial-temporal voltage input. With the Bi2O2Se dendrite device, dendritic neuron model is implemented, and the proposed neural network achieved high recognition rates of 78.3% with the street view house numbers (SVHN) data set.
