Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2528038

Femtojoule optical nonlinearity for deep learning with incoherent illumination

Feng, Qixin [University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000317560090)·Uzundal, Can B. [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)]·Guo, Ruihan [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)] (ORCID:000000029724396X)·Sanborn, Collin [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)] (ORCID:0009000856956091)·Qi, Ruishi [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)] (ORCID:0009000013051104)·Xie, Jingxu [University of California, Berkeley, CA (United States); ShanghaiTech Univ. (China)] (ORCID:0009000096912474)·Zhang, Jianing [University of California, Berkeley, CA (United States); ShanghaiTech Univ. (China)] (ORCID:0009000480909536)·Wu, Junqiao [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)] (ORCID:0000000214980148)·Wang, Feng [University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Kavli Energy NanoScience Institute, Berkeley, CA (United States)] (ORCID:0000000183696194)
View original source ↗Read source document ↗Export metadata ↓

Abstract

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.

Explore related subjects

36 MATERIALS SCIENCE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Feng, Qixin [University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000317560090), Uzundal, Can B. [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)], Guo, Ruihan [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)] (ORCID:000000029724396X), Sanborn, Collin [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)] (ORCID:0009000856956091), Qi, Ruishi [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)] (ORCID:0009000013051104), Xie, Jingxu [University of California, Berkeley, CA (United States); ShanghaiTech Univ. (China)] (ORCID:0009000096912474), Zhang, Jianing [University of California, Berkeley, CA (United States); ShanghaiTech Univ. (China)] (ORCID:0009000480909536), Wu, Junqiao [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)] (ORCID:0000000214980148), Wang, Feng [University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Kavli Energy NanoScience Institute, Berkeley, CA (United States)] (ORCID:0000000183696194). 2025-01-31. Femtojoule optical nonlinearity for deep learning with incoherent illumination. https://doi.org/10.1126/sciadv.ads4224

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Cyclic moisture reactivation of calcium sorbents for long duration thermochemical energy storage

The transition to a flexible and reliable energy infrastructure, using electro-thermal energy generation technologies such as geothermal, concentrated solar power, and nuclear, usually demands simultaneous advancement of thermal energy storage (TES) to support on-demand electricity generation and industrial applications while mitigating the inherent intermittency of renewable energy sources and power outages from direct energy generation. Among TES technologies, thermochemical energy storage (TCES) based on calcium looping emerges as a compelling high-power energy storage candidate due to its high reaction enthalpy, compatibility with elevated operating temperatures, and abundance of low-cost materials. However, the long-term durability of calcium-based sorbents for TCES is hindered by surface sintering and particle aggregation, leading to performance degradation over repeated thermal cycles. This study explores a moisture hydration-based strategy to regenerate a degraded calcium sorbent and mitigate performance degradation for long duration TCES. The addition of moisture transforms calcium oxide into calcium hydroxide and produces intercalation water layers, associated with a regenerated surface area and reduced calcium oxide crystallite size. Both these effects are beneficial in restoring the sorbents' reactivity for carbonization. Additionally, an optimized hydration-assisted reactivation protocol balances the recovered energy storage capacity with heating penalty required for moisture removal from hydrated samples, resulting in an enhanced energy storage capacity up to 176% compared to benchmark sorbents that undergo cycling without reactivation after 60 cycles. In conclusion, these results highlight the potential of hydration-assisted reactivation to enhance the long-term performance of TCES, providing an effective pathway to advancing electro-thermal storage technologies.

36 MATERIALS SCIENCE↗