电子
物理
探测量子效率
像素
探测器
光学
计算机科学
人工智能
图像(数学)
图像质量
量子力学
作者
Jingrui Wei,Kalani Moore,Benjamin Bammes,Benjamin Levin,Nicholas Hagopian,Ryan Jacobs,Dane Morgan,Paul M. Voyles
标识
DOI:10.1093/micmic/ozad132
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI