Spiking neurons from tunable Gaussian heterojunction transistors
- Authors
- Beck, Megan E.; Shylendra, Ahish; Sangwan, Vinod K.; Guo, Silu; Rojas, William A. Gaviria; Yoo, Hocheon; Bergeron, Hadallia; Su, Katherine; Trivedi, Amit R.; Hersam, Mark C.
- Issue Date
- Mar-2020
- Publisher
- NATURE PUBLISHING GROUP
- Citation
- NATURE COMMUNICATIONS, v.11, no.1
- Journal Title
- NATURE COMMUNICATIONS
- Volume
- 11
- Number
- 1
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/78502
- DOI
- 10.1038/s41467-020-15378-7
- ISSN
- 2041-1723
- Abstract
- Spiking neural networks exploit spatiotemporal processing, spiking sparsity, and high interneuron bandwidth to maximize the energy efficiency of neuromorphic computing. While conventional silicon-based technology can be used in this context, the resulting neuron-synapse circuits require multiple transistors and complicated layouts that limit integration density. Here, we demonstrate unprecedented electrostatic control of dual-gated Gaussian heterojunction transistors for simplified spiking neuron implementation. These devices employ wafer-scale mixed-dimensional van der Waals heterojunctions consisting of chemical vapor deposited monolayer molybdenum disulfide and solution-processed semiconducting single-walled carbon nanotubes to emulate the spike-generating ion channels in biological neurons. Circuits based on these dual-gated Gaussian devices enable a variety of biological spiking responses including phasic spiking, delayed spiking, and tonic bursting. In addition to neuromorphic computing, the tunable Gaussian response has significant implications for a range of other applications including telecommunications, computer vision, and natural language processing. Designing high performance, scalable, and energy efficient spiking neural networks remains a challenge. Here, the authors utilize mixed-dimensional dual-gated Gaussian heterojunction transistors from single-walled carbon nanotubes and monolayer MoS2 to realize simplified spiking neuron circuits.
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