Single-shot matrix-matrix photonic processor based on spatial-spectral hypermultiplexed parallel diffraction.

Chao Luan, Ronald Davis Iii, Zaijun Chen, Dirk Englund, Ryan Hamerly

Abstract read

In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Chao LuanResearch Laboratory of Electronics, MIT, Cambridge, MA, USA. chaoluan@mit.edu.ORCID http://orcid.org/0009-0004-0177-8452

Ronald Davis IiiResearch Laboratory of Electronics, MIT, Cambridge, MA, USA.

Zaijun ChenDepartment of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA.

Dirk EnglundResearch Laboratory of Electronics, MIT, Cambridge, MA, USA. englund@mit.edu.ORCID http://orcid.org/0000-0002-1043-3489

Ryan HamerlyResearch Laboratory of Electronics, MIT, Cambridge, MA, USA. rhamerly@mit.edu.ORCID http://orcid.org/0000-0003-4976-2236

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ever-increasing data demand craves advancements in high-speed and energy-efficient computing hardware. Analog optical neural network (ONN) processors have emerged as a promising solution, offering benefits in bandwidth and energy consumption. However, existing ONN processors exhibit limited computational parallelism, and while certain architectures achieve high parallelism, they encounter serious scaling up roadblocks for large-scale implementation. Here, we introduce a spatial-wavelength-temporal hyper-multiplexed ONN processor, which is based on parallel diffractive beam routing. The architecture supports high three-dimensional data, high O(N

Identifiers

PMID41530160

DOI10.1038/s41467-026-68452-x

PMCPMC12804968

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.