# In-situ Self-optimization of Quantum Dot Emission for Lasers by Machine-Learning Assisted Epitaxy

arXiv: https://arxiv.org/abs/2411.00332 · HTML: https://mlsanity.com/paper/2411.00332/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 1 of 1)

- [Clustering with t-SNE, provably](https://mlsanity.com/paper/1706.02582/) (2017)

## Built on by (most cited first, 1 of 1)

- [Unified machine-learning framework for property prediction and time-evolution simulation of strained alloy microstructure](https://mlsanity.com/paper/2507.21760/) (2025)

## Abstract (arXiv)

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, we integrate in-situ reflection high-energy electron diffraction (RHEED) 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. We successfully optimized InAs QDs on GaAs substrates, with a 3.2-fold increase in PL intensity and a reduction in full width at half maximum (FWHM) from 36.69 meV to 28.17 meV under initially suboptimal growth conditions. Our 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/cm2 at room temperature, an excellent performance comparable to samples grown through traditional manual multi-parameter optimization methods. These results mark a significant step toward intelligent, low-cost, and reproductive light emitters production.