ArticleNature communications2024
Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Interpretable self-driving sputtering epitaxy reveals human-usable growth rules for β-GaNature communications · 2026Article
- On-demand growth of semiconductor heterostructures guided by physics-informed machine learning.Science advances · 2026Article
- Review
- A review of applications of machine learning in quantum dots research.Discover nano · 2026Review
- Computational and experimental pathways to next-generation ultrawide-band-gap oxide semiconductors.Nano convergence · 2026Review
- Review
- Experimental design and performance evaluation of a solar panel-based visible light communication system for greenhouse applications.Scientific reports · 2025Article
- Epitaxy of Emerging Materials and Advanced Heterostructures for Microelectronics and Quantum Sciences.Small methods · 2025Article
- Revisiting the Marcus inverted regime: modulation strategies for photogenerated ultrafast carrier transfer from semiconducting quantum dots to metal oxides.RSC advances · 2025Review
- Real-Time Self-Optimization of Quantum Dot Laser Emissions During Machine Learning-Assisted Epitaxy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Reconfigurable terahertz optoelectronic logic through charge-density-wave phase engineering.Nature communications · 2025Article
- Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
The applications of self-assembled InAs/GaAs quantum dots (QDs) for lasers and single photon sources strongly rely on their density and quality. Establishing the process parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a multidimensional optimization challenge, usually addressed through time-consuming and iterative trial-and-error. Here, we report a real-time feedback control method to realize the growth of QDs with arbitrary density, which is fully automated and intelligent. We develop a machine learning (ML) model named 3D ResNet 50 trained using reflection high-energy electron diffraction (RHEED) videos as input instead of static images and providing real-time feedback on surface morphologies for process control. As a result, we demonstrate that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5 × 10
Identifiers
What OpenQuestion holds
Registered trials
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.