Evidence map›Paper›PMID 39635175›Full record

ReviewNanophotonics (Berlin, Germany)2022

Photonic (computational) memories: tunable nanophotonics for data storage and computing.

Chuanyu Lian, Christos Vagionas, Theonitsa Alexoudi, Nikos Pleros, Nathan Youngblood, Carlos Ríos

Abstract readReview
In one paragraph

Review in Nanophotonics (Berlin, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. PZT optical memristors.Nature communications · 2025
    Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Tunable nanophotonics.Nanophotonics (Berlin, Germany) · 2022
    Article
  17. Article
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

6 authors.

Chuanyu LianDepartment of Materials Science & Engineering, University of Maryland, College Park, MD, USA.
Christos VagionasDepartment of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Theonitsa AlexoudiDepartment of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Nikos PlerosDepartment of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Nathan YoungbloodDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
Carlos RíosDepartment of Materials Science & Engineering, University of Maryland, College Park, MD, USA.ORCID https://orcid.org/0000-0001-6859-5491

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The exponential growth of information stored in data centers and computational power required for various data-intensive applications, such as deep learning and AI, call for new strategies to improve or move beyond the traditional von Neumann architecture. Recent achievements in information storage and computation in the optical domain, enabling energy-efficient, fast, and high-bandwidth data processing, show great potential for photonics to overcome the von Neumann bottleneck and reduce the energy wasted to Joule heating. Optically readable memories are fundamental in this process, and while light-based storage has traditionally (and commercially) employed free-space optics, recent developments in photonic integrated circuits (PICs) and optical nano-materials have opened the doors to new opportunities on-chip. Photonic memories have yet to rival their electronic digital counterparts in storage density; however, their inherent analog nature and ultrahigh bandwidth make them ideal for unconventional computing strategies. Here, we review emerging nanophotonic devices that possess memory capabilities by elaborating on their tunable mechanisms and evaluating them in terms of scalability and device performance. Moreover, we discuss the progress on large-scale architectures for photonic memory arrays and optical computing primarily based on memory performance.

Indexed as

optoelectronicsphotonic computingphotonic memorytunable nanophotonics

Identifiers

PMID39635175
PMCPMC11501226

What OpenQuestion holds

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Registered trials

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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.