What Can We Learn from the Shapes of Secondary Electron Puddles on Direct Electron Detectors?

Abstract

Direct electron detectors are a recent breakthrough in transmission electron microscopy that have opened up the possibility of super-resolution microscopy even at low doses. These detectors use CMOS sensors that allow fast read-outs, such that individual electron events (the "puddles" formed by the secondary electrons) can be recorded.While single event detection based imaging, referred to as electron counting, is starting to be widely adopted, several challenges still remain. One of these is, accurately identifying the entry point of the primary electron given the secondary electron puddle produced by it. Succeeding here reduces imaging noise, improves detector efficiency and allows for super-pixel accurate reconstruction [1]. It has been shown that these secondary electron puddles can vary greatly in shape and size [2]. While several techniques have been proposed [2], there is no consensus on which of these is optimal and why they would be better. Here, we investigate the shape and size of these secondary electron puddles to better understand how electron counting can be improved.We present results from several shadow experiments, where a beam blanker is used to block a part of the electron beam, performed using the DE-16 detector (Direct Electron, LP, San Diego, CA, USA) on a JEM-2200FS transmission electron microscope (JEOL USA Inc, Peabody, MA, USA). These experiments are used to study the differences between the secondary electron puddles in the exposed region and the shadow of the beam blanker (Fig. 1). Indeed in our experiments significant number of secondary electron puddles could be seen in the shadow region, which was previously conjectured to arise from electrons scattered back into the detector from the detector's surface, electrons scattered by the microscope column, and Xray photons generated when electrons are absorbed by the beam blanker and the microscope column [3].We found a statistically significant difference between the distribution of sizes of secondary electron puddles in the exposed and shadow regions (Fig. 2a). The puddles in the shadow region have larger variance in size than puddles in the exposed region. Although the secondary electron puddles vary greatly in shape, they appear to share common morphological features (Fig. 2c,d). Further, there appears no statistically significant differences in the shape of electron puddles found in the exposed versus shadow regions. We quantify the frequency of such stray electrons for the DE-16 detector (Fig. 2b), and speculate on possible strategies for noise reduction.

A data reduction and compression description for high throughput time-resolved electron microscopy

“…In L2 reduction, a summary statistic, such as mean, maximum or sum of ADU (analog-digital unit), is extracted for each electron puddle. Preliminary studies suggest that such information may correlate with whether a measured electron was elastically or inelastically scattered 1 . The sparse puddle features are then packed into a dense format and the binary map and the dense puddle features are independently compressed.…”

“…Reduction to L4 involves reducing puddles to the single pixel, which ideally contains the entry point of the incident electron. However, there is no clear consensus on the best approximation strategy for determining the entry point, given a secondary electron puddle 1 . The three common strategies are to reduce the puddle to 1) the pixel that has the maximum intensity, 2) the pixel intensity weighted centroid (center of mass) or 3) the unweighted centroid of the puddle.…”

“…Nearly all the useful information in a single raw detector image is contained within “secondary electron puddles”, each of which is digitized from the cloud of secondary charged particles formed in the wake of individual high energy electrons passing through the detector’s sensor. While the size and shape of secondary electron puddles contain some information 1 , localizing the entry point of the incident electron from its electron cloud already noticeably improves the spatial resolution of the image. To accurately localize these electron puddles they must be spatiotemporally well separated (by increasing the frame rate or reducing the incident electron flux), thereby reducing the so-called coincidence loss 2 .…”