ArticleNature communications2025
Spectral convolutional neural network chip for in-sensor edge computing of incoherent natural light.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
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- Compact Snapshot Hyperspectral Imaging With Neural Dispersion-Engineered Metalens.Nanophotonics (Berlin, Germany) · 2026Article
- Reconfigurable ferroelectric transistor array for embodied neuromorphic vision with hardware-native sensing, computing, and activation.Science advances · 2026Article
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- Theoretical Modeling and Experimental Study on Low-Altitude Slow-Small Target (LSS) Detection Based on Broadband Spectral Modulation Imaging.Sensors (Basel, Switzerland) · 2026Article
- A reconfigurable photosensitive split-floating-gate memory for neuromorphic computing and nonlinear activation.Nature communications · 2026Article
- Metaoptics merging computational optics and optical computing toward intelligent visual perception.Science advances · 2026Review
- On-chip quantum states generation by incoherent light.Nature communications · 2025Article
- Green hydrogen generation using mushroom-like shape bismuth molybdate-bismuth oxide/poly(1 H-pyrrole) core-shell nanocomposite from sanitation water.Scientific reports · 2025Article
- Spectral Demodulation of Mixed-Linewidth FBG Sensor Networks Using Cloud-Based Deep Learning for Land Monitoring.Sensors (Basel, Switzerland) · 2025Article
- AIoT-Based Eyelash Extension Durability Evaluation Using LabVIEW Data Analysis.Sensors (Basel, Switzerland) · 2025Article
- Spectral convolutional neural network chip for in-sensor edge computing of incoherent natural light.Nature communications · 2025Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optical neural networks are considered next-generation physical implementations of artificial neural networks, but their capabilities are limited by on-chip integration scale and requirement for coherent light sources. This study proposes a spectral convolutional neural network (SCNN) with matter meta-imaging. The optical convolutional layer is implemented by integrating very large-scale and pixel-aligned spectral filters on CMOS image sensor. It facilitates highly parallel spectral vector-inner products of incident incoherent natural light i.e., the direct information carrier, which empowers in-sensor optical analog computing at extremely high energy efficiency. To the best of our knowledge, this is the first integrated optical computing utilizing natural light. We employ the same SCNN chip for completely different real-world complex tasks and achieve accuracies of over 96% for pathological diagnosis and almost 100% for face anti-spoofing at video rates. These results indicate a feasible and scalable in-sensor edge computing chip of natural light for various portable terminals.
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Registered trials
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