Highly adaptive and energy efficient neuromorphic computation enabled by deep-spike heterostructure photonic neuro-transistors. (15th December 2022)

• Record Type: Journal Article
• Authors: Cho, Sung Soo; Kim, Jaehyun; Jeong, Sungwoo; Kwon, Sung Min; Jo, Chanho; Kwak, Jee Young; Kim, Dong Hyuk; Cho, Sung Woon; Kim, Yong-Hoon; Park, Sung Kyu
• Is Part Of: Nano energy. Volume 104(2022)Part B
• Publication Date: 2022-12-15
• Subjects: Photonic neuro-transistors -- Deep spike-like -- Heterostructure -- Band-bending -- Synaptic parameters; Nanoscience -- Periodicals; Nanotechnology -- Periodicals; Nanostructured materials -- Periodicals; Power resources -- Technological innovations -- Periodicals; Nanoscience; Nanostructured materials; Nanotechnology; Power resources -- Technological innovations; Periodicals; 621.042
• DOI: 10.1016/j.nanoen.2022.107991
• Languages: English
• ISSNs: 2211-2855

Abstract

Abstract: Recently, neuromorphic photonics using optical signal as a data domain are considered as a promising solution to realize the next generation neural network platform. Here, metal-chalcogenide/metal oxide semiconductor based photonic neuro-transistors with deep spike-like heterostructure are proposed as a highly adaptive and energy efficient neuromorphic device. In particular, the energy band structure of cadmium sulfide (CdS)/amorphous indium-gallium-zinc-oxide ( a -IGZO) heterojunction is engineered via mediating the anion-to-cation ratio of CdS films. It is revealed that the S/Cd ratio is able to determine the work function of the film which consequently causes a variation in the degree of band-bending at the heterointerface. Using a CdS film with optimized S/Cd ratio (CdS1.2 ), deep spike-like heterostructure (DHS) can be constructed which enables efficient accumulation of photo-generated charge carriers and the emulation of biological synaptic functions including long-term potentiation (LTP) and depression (LTD) behaviors. Also, the a -IGZO/CdS1.2 DHS transistor exhibits low non-linearity value for LTP (1.1) and less energy consumption (45.04 pJ). Furthermore, 7 × 7 opteoelectronic neuromorphic arrays are successfully implemented to exhibit possibility of realization of hardware-based weight pixel training. In addition, the a -IGZO/CdS1.2 DHS transistor shows a high accuracy for image pattern recognition (85.96%) based on the artificial neural network simulation, proving the feasibility in the artificial intelligent systems. Graphical Abstract: Photonic neuro-transistors are realized comprising a heterostructure of cadmium sulfide (CdS) light absorbing layer and amorphous indium-gallium-zinc-oxide ( a- IGZO) semiconductor. Deep spike-like heterostructure can be optimized via mediating the energy band structures, demonstrating efficient accumulation of photo-generated charge carriers at heterointerfaces and emulation of biological synaptic functions. Opteoelectronic neuromorphic arrays are also implemented to exhibit highly accurate image pattern recognition. ga1 Highlights: Spike-like heterojunction based synaptic device could realize stable synaptic functions. Deep-level potential well provides sufficient energy states for charge accumulation. Energy-efficient synaptic function is achieved with deep spike heterojunction device.