Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 700

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Photonic Synapses Based on Inorganic Perovskite Quantum Dots for Neuromorphic Computing

Wang, Yan, Lv, Ziyu, Chen, Jinrui, Wang, Zhanpeng, Zhou, Ye, Zhou, Li, Chen, Xiaoli, Han, Su‐Ting·2018·Advanced Materials

Abstract

Inspired by the biological neuromorphic system, which exhibits a high degree of connectivity to process huge amounts of information, photonic memory is expected to pave a way to overcome the von Neumann bottleneck for nonconventional computing. Here, a photonic flash memory based on all‐inorganic CsPbBr 3 perovskite quantum dots (QDs) is demonstrated. The heterostructure formed between the CsPbBr 3 QDs and semiconductor layer serves as a basis for optically programmable and electrically erasable characteristics of the memory device. Furthermore, synapse functions including short‐term plasticity, long‐term plasticity, and spike‐rate‐dependent plasticity are emulated at the device level. The photonic potentiation and electrical habituation are implemented and the synaptic weight exhibits multiple wavelength response from 365, 450, 520 to 660 nm. These results may locate the stage for further thrilling novel advances in perovskite‐based memories.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/adma.201802883

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Cited by

See all 700

Metadata

Authors
Wang, Y., Lv, Z., Chen, J., Wang, Z., Zhou, Y., Zhou, L., Chen, X., & Han, S.
Venue
Advanced Materials
Publisher
Wiley
Published
2018-07-31
Volume
30
Issue
38
Pages
e1802883-e1802883
ISSN
  • 0935-9648 (ISSN-L)
  • 1521-4095
OpenAlex
W2886721396
Cited by
700
References
4
FWCI
23.33
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Photonic Synapses Based on Inorganic Perovskite Quantum Dots for Neuromorphic Computing. https://app.cochat.ai/papers/10.1002/adma.201802883
The paper
Wang, Y., Lv, Z., Chen, J., Wang, Z., Zhou, Y., Zhou, L., Chen, X., & Han, S. (2018). Photonic Synapses Based on Inorganic Perovskite Quantum Dots for Neuromorphic Computing. Advanced Materials. https://doi.org/10.1002/adma.201802883

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.