Evidence map›Paper›PMID 40317655›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Real-Time Self-Optimization of Quantum Dot Laser Emissions During Machine Learning-Assisted Epitaxy.

Chao Shen, Wenkang Zhan, Shujie Pan, Hongyue Hao, Ning Zhuo, Kaiyao Xin, Hui Cong, Chi Xu, Bo Xu, Tien Khee Ng and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. 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. 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

14 authors.

Chao ShenLaboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.
Wenkang ZhanLaboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.
Shujie PanLaboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.
Hongyue HaoCollege of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 101408, China.
Ning ZhuoLaboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.
Kaiyao XinCollege of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 101408, China.
Hui CongCollege of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 101408, China.
Chi XuCollege of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 101408, China.
Bo XuLaboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.
Tien Khee NgPhotonics Laboratory, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
Siming ChenLaboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.
Chunlai XueCollege of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 101408, China.
Zhanguo WangLaboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.
Chao ZhaoLaboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.ORCID https://orcid.org/0000-0002-9582-1068

Funding

Beijing Natural Science Foundation F251036Beijing Natural Science Foundation L248103CAS Project for Young Scientists in Basic Research YSBR-056National Key R&D Program of China 2021YFB2206500National Key R&D Program of China 2023YFB2805900National Natural Science Foundation of China 62274159
6 · The paper itself

Abstract

Traditional methods for optimizing light source emissions rely on a time-consuming trial-and-error approach. While in situ optimization of light source gain media emission during growth is ideal, it has yet to be realized. In this work, in situ reflection high-energy electron diffraction (RHEED) is integrated with machine learning (ML) to correlate the surface reconstruction with the photoluminescence (PL) of InAs/GaAs quantum dots (QDs), which serve as the active region of lasers. A lightweight ResNet-GLAM model is employed for the real-time processing of RHEED data as input, enabling effective identification of optical performance. This approach guides the dynamic optimization of growth parameters, allowing real-time feedback control to adjust the QDs emission for lasers. InAs QDs on GaAs substrates are successfully optimized, with a 3.2-fold increase in PL intensity and a reduction in full width at half maximum (FWHM) from 36.69 to 28.17 meV. Automated, in situ self-optimized lasers with 5-layer InAs QDs achieved electrically pumped continuous-wave operation at 1240 nm with a low threshold current of 150 A cm

Indexed as

lasermachine learningmolecular beam epitaxyquantum dotsreal‐time control

Identifiers

PMID40317655
PMCPMC12279205

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.