Evidence map›Paper›PMID 35273302›Full record

ArticleScientific reports2022

A photonic complex perceptron for ultrafast data processing.

Mattia Mancinelli, Davide Bazzanella, Paolo Bettotti, Lorenzo Pavesi

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

4 authors.

Mattia MancinelliNanoLab, Department of Physics, University of Trento, Via Sommarive 14, 38123, Trento, Italy. mattia.mancinelli@unitn.it.
Davide BazzanellaNanoLab, Department of Physics, University of Trento, Via Sommarive 14, 38123, Trento, Italy.
Paolo BettottiNanoLab, Department of Physics, University of Trento, Via Sommarive 14, 38123, Trento, Italy.
Lorenzo PavesiNanoLab, Department of Physics, University of Trento, Via Sommarive 14, 38123, Trento, Italy.

Funding

European Research Council 788793Ministero dell'Istruzione, dell'Università e della Ricerca 20177 PSCKT
6 · The paper itself

Abstract

In photonic neural network a key building block is the perceptron. Here, we describe and demonstrate a complex-valued photonic perceptron that combines time and space multiplexing in a fully passive silicon photonics integrated circuit to process data in the optical domain. A time dependent input bit sequence is broadcasted into a few delay lines and detected by a photodiode. After detection, the phases are trained by a particle swarm algorithm to solve the given task. Since only the phases of the propagating optical modes are trained, signal attenuation in the perceptron due to amplitude modulation is avoided. The perceptron performs binary pattern recognition and few bit delayed XOR operations up to 16 Gbps (limited by the used electronics) with Bit Error Rates as low as [Formula: see text].

Identifiers

PMID35273302
PMCPMC8913748

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.