Gabriela Correa, Cornell University

Photo of Gabriela Correa

Scientific data are approaching acquisition volumes larger than ever before. Electron microscopy generates exponentially more information as detector technologies advance. In particular, direct electron detectors (DED) are approaching 200 TB/hour.1 To date, electron microscopy holds the world record for resolution at 39 pm.2 This is attained through solving the NP-hard problem of phase retrieval with ptychographic algorithms. Obtaining this resolution requires relatively radiation-robust materials of near-atomic thinness, limiting applicability of the technique.3 To take advantage of DED for low-dose imaging, the quality and usability of every data point must increase. We present a new method to process DED data with machine-learning and high-performance computing infrastructures. Increasing accuracy is attained while compressing information by orders of magnitude. 1I.J. Johnson et al., "Next-Generation Electron Microscopy Detector Aimed at Enabling New Scanning Diffraction Techniques and Online Data Reconstruction," Microsc. Microanal., vol. 24, no. S1, pp. 166-167, 2018. 2Highest resolution microscope. Guinness World Records, 2018. 3Y. Jiang et al., "Electron ptychography of 2D materials to deep sub-angstrom resolution," Nature, vol. 559, no. 7714, pp. 343-349, 2018.

Abstract Author(s): Gabriela C. Correa, Michael C. Cao, David A. Muller