Details

Autor(en) / Beteiligte

• Teng, Caihong
• Zou, Jihua
• Tang, Xingyu
• Huang, Yixuan
• He, Weijie
• Du, Wen
• Luo, Lingzhi
• Ren, Aobo
• Wu, Jiang
• Wang, Zhiming

Titel

MoS2 as nonlinear optical material for optical neural networks

Ist Teil von

• IEEE journal of selected topics in quantum electronics, 2023-03, Vol.29 (2: Optical Computing), p.1-7

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2023

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Despite the significant advancements in photonic computation in recent years, the inadequacy of optical nonlinearities limits the scalability of optical deep networks (ONNs). Molybdenum disulfide (MoS 2 ), with excellent nonlinear properties, is emerging as a promising candidate for nonlinear processing. Here, we investigate the saturable absorption of MoS 2 by continuous wave lasers and illustrate the capability of MoS 2 as an activation unit for nonlinear mapping in ONNs. Moreover, a simulation-based fully connected neural network is fabricated for mimicking the operation of ONNs and demonstrating image classification. The results show that the recognition accurateness ranged from 89% to 94%, depending on the morphology of MoS 2 . This work provides a guideline for the selection of nonlinear units and opens up the possibility of implementing all-optical neural networks.