Femtojoule optical nonlinearity for deep learning with incoherent illumination

Journal Article · 31 January 2025 · Science Advances

DOI: https://doi.org/10.1126/sciadv.ads4224 · OSTI ID:2528038

Feng, Qixin  ^[1]; Uzundal, Can B. ^[2]; Guo, Ruihan  ^[2]; Sanborn, Collin  ^[2]; Qi, Ruishi  ^[2]; Xie, Jingxu  ^[3]; Zhang, Jianing  ^[3]; Wu, Junqiao  ^[2]; Wang, Feng  ^[4]

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1. University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
2. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)
3. University of California, Berkeley, CA (United States); ShanghaiTech Univ. (China)
4. University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Kavli Energy NanoScience Institute, Berkeley, CA (United States)

Optical neural networks (ONNs) are a promising computational alternative for deep learning due to their inherent massive parallelism for linear operations. However, the development of energy-efficient and highly parallel optical nonlinearities, a critical component in ONNs, remains an outstanding challenge. Here, we introduce a nonlinear optical microdevice array (NOMA) compatible with incoherent illumination by integrating the liquid crystal cell with silicon photodiodes at the single-pixel level. We fabricate NOMA with more than half a million pixels, each functioning as an optical analog of the rectified linear unit at ultralow switching energy down to 100 femtojoules per pixel. With NOMA, we demonstrate an optical multilayer neural network. Our work holds promise for large-scale and low-power deep ONNs, computer vision, and real-time optical image processing.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 2528038
• Journal Information: Science Advances, Journal Name: Science Advances Journal Issue: 5 Vol. 11; ISSN 2375-2548
• Publisher: AAASCopyright Statement
• Country of Publication: United States
• Language: English