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XRL-QNet: an explainable reinforcement learning framework for optimizing and evaluating quantum dots fabrication.

M Irshad Ahamed1, Vivekanandhan A2, Senthil Mahesh P C3

  • 1Deparment of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India.

Nanotechnology
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces XRL-QNet, an AI platform that uses Proximal Policy Optimization (PPO) to automate the fabrication of high-quality semiconductor quantum dots (QDs). This innovation enhances scalability and efficiency for quantum photonics applications.

Keywords:
CNNfabricationmaterialsquantum optimizationreinforcement learningsemiconductorspectral

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Area of Science:

  • Quantum photonics
  • Semiconductor device manufacturing
  • Artificial intelligence in materials science

Background:

  • High-quality, on-demand single-photon sources are crucial for quantum photonics.
  • Semiconductor quantum dots (QDs) offer tunable emission spectra but face challenges in scalable, efficient fabrication due to intrinsic randomness.
  • Current QD measurement techniques lack real-time control, hindering practical implementation.

Purpose of the Study:

  • To develop an automated and scalable method for synthesizing high-performance quantum dots.
  • To address the limitations of manual spectrum analysis and lack of real-time control in QD fabrication.
  • To integrate machine learning for optimizing fabrication parameters and improving QD quality.

Main Methods:

  • Implementation of XRL-QNet, a machine learning platform utilizing Proximal Policy Optimization (PPO).
  • Real-time optimization of fabrication parameters (e.g., substrate temperature, material flux, growth time) during molecular beam epitaxy (MBE).
  • Utilizing CNN autoencoders for latent spectral feature extraction and neural regression for scoring, enhanced by explainable AI (XAI) techniques (SHAP, LIME).

Main Results:

  • XRL-QNet successfully optimizes QD fabrication parameters in real-time.
  • The platform extracts spectral features and provides confidence scores for QD quality assessment.
  • Explainable AI techniques ensure transparency and interpretability in the QD assessment process.

Conclusions:

  • XRL-QNet with PPO optimization enables the synthesis of high-performance QDs tailored for specific quantum applications.
  • The closed-loop system integrates fabrication control with emission spectrum analysis for automated, scalable QD production.
  • This approach represents an innovative advancement in semiconductor quantum dot manufacturing for quantum technologies.