Recent advances in quantum dots （QDs） for classical and non-classical light sources are presented. We have established metal organic chemical vapor deposition （MOCVD） technology for InAs-based QD lasers at 1.3 μm...Recent advances in quantum dots （QDs） for classical and non-classical light sources are presented. We have established metal organic chemical vapor deposition （MOCVD） technology for InAs-based QD lasers at 1.3 μm and achieved ultralow threshold in QD lasers with photonic crystal （PhC） nanocavity. In addition, single photon emitters at 1.55 μm, GaN-based single photon sources operating at 200 K, and high-Q PhC nanocavity have been demonstrated.展开更多
Machine learning is attracting surging interest across nearly all scientific areas by enabling the analysis of large datasets and the extraction of scientific information from incomplete data.Data-driven science is ra...Machine learning is attracting surging interest across nearly all scientific areas by enabling the analysis of large datasets and the extraction of scientific information from incomplete data.Data-driven science is rapidly growing,especially in X-ray methodologies,where advanced light sources and detection technologies produce vast amounts of data that exceed meticulous human inspection capabilities.Despite the increasing demands,the full application of machine learning has been hindered by the need for data-specific optimizations.In this study,we introduce a new deep-learning-based phase retrieval method for imperfect diffraction data.This method provides robust phase retrieval for simulated data and performs well on partially damaged and noisy single-pulse diffraction data from X-ray free-electron lasers.Moreover,the method significantly reduces data processing time,facilitating real-time image reconstructions that are crucial for high-repetition-rate data acquisition.This approach offers a reliable solution to the phase problem to be widely adopted across various research areas confronting the inverse problem.展开更多
基金Special Coordination Funds for Promoting Science and Technology
文摘Recent advances in quantum dots （QDs） for classical and non-classical light sources are presented. We have established metal organic chemical vapor deposition （MOCVD） technology for InAs-based QD lasers at 1.3 μm and achieved ultralow threshold in QD lasers with photonic crystal （PhC） nanocavity. In addition, single photon emitters at 1.55 μm, GaN-based single photon sources operating at 200 K, and high-Q PhC nanocavity have been demonstrated.
基金supported by the National Research Foundation of Korea(Grant Nos.RS-2022-NR068223 and RS-2024-00346711).
文摘Machine learning is attracting surging interest across nearly all scientific areas by enabling the analysis of large datasets and the extraction of scientific information from incomplete data.Data-driven science is rapidly growing,especially in X-ray methodologies,where advanced light sources and detection technologies produce vast amounts of data that exceed meticulous human inspection capabilities.Despite the increasing demands,the full application of machine learning has been hindered by the need for data-specific optimizations.In this study,we introduce a new deep-learning-based phase retrieval method for imperfect diffraction data.This method provides robust phase retrieval for simulated data and performs well on partially damaged and noisy single-pulse diffraction data from X-ray free-electron lasers.Moreover,the method significantly reduces data processing time,facilitating real-time image reconstructions that are crucial for high-repetition-rate data acquisition.This approach offers a reliable solution to the phase problem to be widely adopted across various research areas confronting the inverse problem.
