Auto-thresholding for Unbiased Electron Counting.

Abstract

As interest in fast real-space frame-rate scanning transmission electron microscopy for both structural and functional characterisation of materials increases, so does the need for precise and fast electron detection techniques. Electron counting, with monolithic, segmented, or 4D detectors, has been explored for many years. Recent studies have shown that a retrofittable signal digitiser for a monolithic or segmented detector can be a sustainable and accessible way to enhance the performance of existing detectors, especially for imaging at fast scan speeds. Since such signal digitisation uses a threshold on the gradient of the detector signal to identify electron events, appropriate threshold choice is key. Previously, this threshold has been set manually by the operator and is therefore inherently susceptible to human bias. In this work, we introduce an auto-thresholding approach for electron counting to determine the optimal threshold by maximising the difference in identified counts from a stream with real electron events and a stream with only noise. This leads to easier operation, increased throughput and eliminates human bias in signal digitisation.