Evidence map›Paper›PMID 37479712›Full record

ArticleNature communications2023

Optofluidic memory and self-induced nonlinear optical phase change for reservoir computing in silicon photonics.

Chengkuan Gao, Prabhav Gaur, Dhaifallah Almutairi, Shimon Rubin, Yeshaiahu Fainman

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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.

Chengkuan GaoDepartment of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA.ORCID http://orcid.org/0000-0003-3561-1664
Prabhav GaurDepartment of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA.ORCID http://orcid.org/0000-0001-9750-4608
Dhaifallah AlmutairiDepartment of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA.
Shimon RubinDepartment of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA. rubin.shim@gmail.com.ORCID http://orcid.org/0000-0002-4804-4841
Yeshaiahu FainmanDepartment of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA.

Funding

United States Department of Defense | Defense Advanced Research Projects Agency (DARPA) HR00112090009
6 · The paper itself

Abstract

Nanophotonics allows to employ light-matter interaction to induce nonlinear optical effects and realize non-conventional memory and computation capabilities, however to date, light-liquid interaction was not considered as a potential mechanism to achieve computation on a nanoscale. Here, we experimentally demonstrate self-induced phase change effect which relies on the coupling between geometric changes of thin liquid film to optical properties of photonic waveguide modes, and then employ it for neuromorphic computing. In our optofluidic silicon photonics system we utilize thermocapillary-based deformation of thin liquid film capable to induce nonlinear effect which is more than one order of magnitude higher compared to the more traditional heat-based thermo-optical effect, and allowing operation as a nonlinear actuator and memory element, both residing at the same compact spatial region. The resulting dynamics allows to implement Reservoir Computing at spatial region which is approximately five orders of magnitude smaller compared to state-of-the-art experimental liquid-based systems.

Identifiers

PMID37479712
PMCPMC10362060

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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.