The manipulation and control of nanoscale magnetic spin textures are of rising interest as they are potential foundational units in next-generation computing paradigms.Achieving this requires a quantitative understand...The manipulation and control of nanoscale magnetic spin textures are of rising interest as they are potential foundational units in next-generation computing paradigms.Achieving this requires a quantitative understanding of the spin texture behavior under external stimuli using in situ experiments.Lorentz transmission electron microscopy(LTEM)enables real-space imaging of spin textures at the nanoscale,but quantitative characterization of in situ data is extremely challenging.Here,we present an AI-enabled phase-retrieval method based on integrating a generative deep image prior with an image formation forwardmodel for LTEM.Our approach uses a single out-of-focus image for phase retrieval and achieves significantly higher accuracy and robustness to noise compared to existing methods.Furthermore,our method is capable of isolating sample heterogeneities from magnetic contrast,as shown by application to simulated and experimental data.This approach allows quantitative phase reconstruction of in situ data and can also enable near real-time quantitative magnetic imaging.展开更多
基金supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,Materials Sciences and Engineering Divisionsupported by the U.S.Department of Energy,Office of Science,under Contract No.DE-AC02-06CH11357M.J.C also acknowledges support from Argonne LDRD 2021-0090-AutoPtycho:Autonomous,Sparse-sampled Ptychographic Imaging.
文摘The manipulation and control of nanoscale magnetic spin textures are of rising interest as they are potential foundational units in next-generation computing paradigms.Achieving this requires a quantitative understanding of the spin texture behavior under external stimuli using in situ experiments.Lorentz transmission electron microscopy(LTEM)enables real-space imaging of spin textures at the nanoscale,but quantitative characterization of in situ data is extremely challenging.Here,we present an AI-enabled phase-retrieval method based on integrating a generative deep image prior with an image formation forwardmodel for LTEM.Our approach uses a single out-of-focus image for phase retrieval and achieves significantly higher accuracy and robustness to noise compared to existing methods.Furthermore,our method is capable of isolating sample heterogeneities from magnetic contrast,as shown by application to simulated and experimental data.This approach allows quantitative phase reconstruction of in situ data and can also enable near real-time quantitative magnetic imaging.
