Bayesian Optimized Design for O-band Bottom-up Grown Microring Lasers

  • Mihir Rajendra Athavale (Creator)
  • Ruqaiya Al-Abri (Creator)
  • Stephen Church (Creator)
  • Wei Wen Wong (Creator)
  • Andre KY Low (Creator)
  • Hark Hoe Tan (Creator)
  • Kedar Hippalgaonkar (Creator)
  • Patrick Parkinson (Creator)

Dataset

Description

Datasets for "Bayesian Optimized Design for O-band Bottom-up Grown Microring Lasers"These datasets support the multi-objective Bayesian optimization (MOBO) of bottom-up grown InP/InAsP multi-quantum well (MQW) microring lasers. The optimization focuses on three key objectives: minimizing the lasing threshold, targeting O-band lasing wavelengths, and maximizing yield. They include microring growth and geometry parameters, performance metrics, and power-dependent photoluminescence (PL) spectroscopy data. The datasets are organized into three components: (1) the training dataset, (2) the optimized and Design of Experiments (DoE) dataset, and (3) the remeasurement dataset.1. Model Training DatasetFile: 'Training_Dataset.pkl' This dataset was used to train the surrogate model used in the optimization process. Contents:Growth Parameters:Number of quantum wells Quantum well growth temperatureAs/P ratio (estimated from precursor flow rates in the vapor phase)V/III ratio during InP barrier growthCapping layer growth durationGeometry Parameters:MQW Microring diameterPitch (center-to-center spacing between MQW microrings)Performance Metrics:Median lasing threshold across a field (a group of nominally identical MQW microrings)Median lasing wavelength across a fieldYield: Ratio of lasing MQW microrings to total MQW microrings in the fieldAssociated Data:Lasing threshold and wavelength values were derived from power-dependent PL spectroscopy.Power-dependent PL spectroscopy data: 'Training_pdep.h5'Metadata (e.g., experimental conditions, instrument settings): 'settings_training.h5'2. Optimized and DoE DatasetFile: 'Opt_&_DoE_Dataset.pkl' This dataset contains measurements from MQW microring structures designed through two approaches: MOBO and a structured DoE strategy. Contents:Growth and Geometry Parameters:Same structure and format as in the training datasetPerformance Metrics:Identical to those in the training dataset.Associated Data:Power-dependent PL spectroscopy data: 'Opt_&_DoE_pdep.h5'Metadata (e.g., experimental conditions): 'settings_Opt_DoE_remeasurements.h5'3. Remeasurement DatasetThis dataset includes remeasurements of the best-performing MQW microring sample from the training dataset. These remeasurements serve to validate the consistency and reproducibility of lasing threshold and wavelength performance under updated experimental conditions.Files:'remeasured_pdep.h5': Power-dependent PL spectroscopy data'remeasured_sample_D.pkl': Summary of lasing threshold, wavelength and yield'settings_Opt_DoE_remeasurements.h5': Metadata Simultaneous improvements in all three performance metrics were observed in the optimized MQW microring lasers, validating the effectiveness of the MOBO framework.
Date made available20 Jun 2025
PublisherUniversity of Manchester Figshare

Keywords

  • Integrated Photonics
  • III-V Microring Lasers
  • High-throughput Characterization
  • Multi-objective Bayesian Optimization
  • Design of Experiments

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