Deep Learning Approach for High-accuracy Electron Counting of Monolithic Active Pixel Sensor-type Direct Electron Detectors at Increased Electron Dose

Electron counting can be performed algorithmically for monolithic active pixel sensor direct electron detectors to eliminate readout noise and Landau noise arising from the variability in the amount of deposited energy for each electron. Errors in existing counting algorithms include mistakenly counting a multielectron strike as a single electron event, and inaccurately locating the incident position of the electron due to lateral spread of deposited energy and dark noise. Here, we report a supervised deep learning (DL) approach based on Faster region-based convolutional neural network (R-CNN) to recognize single electron events at varying electron doses and voltages. The DL approach shows high accuracy according to the near-ideal modulation transfer function (MTF) and detector quantum efficiency for sparse images. It predicts, on average, 0.47 pixel deviation from the incident positions for 200 kV electrons versus 0.59 pixel using the conventional counting method. The DL approach also shows better robustness against coincidence loss as the electron dose increases, maintaining the MTF at half Nyquist frequency above 0.83 as the electron density increases to 0.06 e-/pixel. Thus, the DL model extends the advantages of counting analysis to higher dose rates than conventional methods.

Electron crystallography of chiral and non-chiral small molecules

Electron crystallography has recently gained attentions in multiple fields of research, as it has been demonstrated to determine atomic structures for inorganic, organic, and macromolecular materials from nano-sized crystals that were not amenable to conventional X-ray crystallography. Here, we demonstrate continuous-rotation microcrystal electron diffraction (microED) in a 200 kV transmission electron microscope using a DE-64 camera-a low-noise direct electron detector that can accommodate a linear response up to ∼1200 electrons per pixel per second at 20 fps with 2x-hardware-binning, making it ideal for acquisition of high-quality diffraction patterns. We have used this method and camera to determine a 0.75 Å structure of an organic molecule, biotin, with an exceptional goodness-of-fit, as well as a 0.88 Å structure of a chiral molecule, L-serine.

Electron backscattered diffraction using a new monolithic direct detector: High resolution and fast acquisition

A monolithic active pixel sensor based direct detector that is optimized for the primary beam energies in scanning electron microscopes is implemented for electron back-scattered diffraction (EBSD) applications. The high detection efficiency of the detector and its large array of pixels allow sensitive and accurate detection of Kikuchi bands arising from primary electron beam excitation energies of 4 keV to 28 keV, with the optimal contrast occurring in the range of 8-16 keV. The diffraction pattern acquisition speed is substantially improved via a sparse sampling mode, resulting from the acquisition of a reduced number of pixels on the detector. Standard inpainting algorithms are implemented to effectively estimate the information in the skipped regions in the acquired diffraction pattern. For EBSD mapping, an acquisition speed as high as 5988 scan points per second is demonstrated, with a tolerable fraction of indexed points and accuracy. The collective capabilities spanning from high angular resolution EBSD patterns to high speed pattern acquisition are achieved on the same detector, facilitating simultaneous detection modalities that enable a multitude of advanced EBSD applications, including lattice strain mapping, structural refinement, low-dose characterization, 3D-EBSD and dynamic in situ EBSD.

Automated specimen search in cryo-TEM observation with DIFF-defocus imaging

We have developed an automated specimen search algorithm for cryo-electron microscopy imaging of ice-embedded single particles suspended across regularly spaced holes. To maximize the particle visibility under a low electron exposure rate condition, specimen searching is carried out in diffraction mode. However, images in diffraction mode contain significant pincushion distortion, making it difficult to computationally predict the locations of the regularly spaced holes. We have implemented a distortion-correction mechanism to restore the primitive distortion-free image and a correlation-based algorithm to accurately determine the periodicity of the holes. A stage-shift method to optimize positional reproducibility is also implemented. Addition of our algorithms to the JADAS software for automated transmission electron microscopy data acquisition has significantly improved the accuracy of specimen search.