Defect-engineered 2D Bi<sub>2</sub>Se<sub>3</sub>-based broadband optoelectronic synapses with ultralow energy consumption for neuromorphic computing
Optoelectronic synapses (OES) inspired by the human brain have gained attention in addressing the von Neumann bottleneck facing traditional computing. Numerous candidates, including topological insulators and other 2D materials, have been used to fabricate OES devices with different degrees of success. Se vacancies commonly appearing in epitaxially grown Bi<sub>2</sub>Se<sub>3</sub> and importantly the ability to modulate the vacancies by changing the growth temperature make it a worthy candidate to construct an OES system. The vacancies effectively trap and release charges, leading to persistent photoconductivity, which is the mechanism behind OES operation. A defect-induced Bi<sub>2</sub>Se<sub>3</sub>-based synapse using an ultrathin layer grown by chemical vapor deposition is shown herein to successfully demonstrate basic synapse characteristics such as paired-pulse facilitation (PPF), short-term and long-term memory, and learning-relearning behavior. This OES device shows a very high PPF index of 201.7%, a long memory retention time of 523.1 s, and an ultralow energy consumption of 9.2 fJ per spike, which is at the low end of the 1-100 fJ range for biological systems. Density functional theory simulations reinforce the definite role of trap centers induced by the Se vacancies in the device operation. Our device realizes a high recognition accuracy of 90.12% for MNIST handwritten digital images in simulations based on an artificial neural network algorithm. The exceptional results achieved here show the potential of Bi<sub>2</sub>Se<sub>3</sub> for synaptic applications and pave the way for exploiting its potential in future high-performance neuromorphic computing and other artificial visual perception systems.
Authors
- P. K. Giri (ORCID: https://orcid.org/0000-0003-2020-4249)
- Meyya Meyyappan (ORCID: https://orcid.org/0000-0001-9202-412X)
- Sanju Nandi
- Sirsendu Ghosal
Institutions
- Indian Institute of Technology Guwahati (IN)
Publication Details
- Journal
- Materials Horizons
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d4mh01625d
- Citations
- 11
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 27.29
Funders
- Indian Institute of Technology Guwahati
- Science and Engineering Research Board