期刊
NANOPHOTONICS
卷 11, 期 17, 页码 4073-4088出版社
WALTER DE GRUYTER GMBH
DOI: 10.1515/nanoph-2022-0099
关键词
all-optical deep neural network; chalcogenide reconfigurable photonics; ultra-fast dynamic response of phase change material
资金
- Agency for Science, Technology and Research (A*STAR) under the Advanced Manufacturing and Engineering (AME) [A18A7b0058]
- Presidential Early Career Award for Scientist andEngineers (PECASE) [FA9550-20-1-0193]
- Fundacio Cellex
- Fundacio Mir-Puig
- Generalitat de Catalunya through CERCA
- [IJC2018-037384-I]
- [MCIN/AEI/10.13039/501100011033]
The study demonstrated a passive all-chalcogenide all-optical perceptron scheme utilizing the nonlinear response of Ge2Sb2Te5 to femtosecond laser pulses. By testing different laser excitations, the designed NLAF showed a sigmoidal response with a dynamic range of -9.7 dB. The perceptron efficiently dissipated heat using AlN material and set nonvolatile weights through Sb2S3-tuned MZIs, achieving energy-efficient all-optical neural classifications at rates greater than 1 GHz.
We demonstrate a passive all-chalcogenide all-optical perceptron scheme. The network's nonlinear activation function (NLAF) relies on the nonlinear response of Ge2Sb2Te5 to femtosecond laser pulses. We measured the sub-picosecond time-resolved optical constants of Ge2Sb2Te5 at a wavelength of 1500 nm and used them to design a high-speed Ge2Sb2Te5-tuned microring resonator all-optical NLAF. The NLAF had a sigmoidal response when subjected to different laser fluence excitation and had a dynamic range of -9.7 dB. The perceptron's waveguide material was AlN because it allowed efficient heat dissipation during laser switching. A two-temperature analysis revealed that the operating speed of the NLAF is <= 1 ns. The percepton's nonvolatile weights were set using low-loss Sb2S3-tuned Mach Zehnder interferometers (MZIs). A three-layer deep neural network model was used to test the feasibility of the network scheme and a maximum training accuracy of 94.5% was obtained. Weconclude that combining Sb2S3-programmed MZI weights with the nonlinear response of Ge2Sb2Te5 to femtosecond pulses is sufficient to perform energy-efficient all-optical neural classifications at rates greater than 1 GHz.
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