Mining for hidden information in scanning electron micrograph subpixels

fiics Incorporated是带电粒子束（CPB）显微镜（通常是扫描电子显微镜（sem）和聚焦离子束显微镜（FIBs））仪器的领导者，并在通常向用户提供的图像中确定了未开发的信息源：他们开发了一种方法来获取和传达包含在标准像素中的“次时间”信息，该信息是通过标准“停留时间”和一次或多次线平均/积分通过获得的。停留时间和线平均设置被认为是定义CPB扫描的“标准”参数，但是允许图像处理功能以时间和空间分辨率的方式访问这种次时间像素信息是一项新的创新，并以一种称为“高，宽子像素”的格式传达。在这个项目中，我们将**应用包括卷积神经网络、支持向量机和**统计方法在内的分类方法来表征SubPixel信息，并寻找与其他**多传感器数据的相关性，并设计基于实时分析该**信息的策略来加速获取。

Fibics Incorporated is a leader in the instrumentation of charged particle beam (CPB) microscopes (typically**Scanning Electron Microscopes (SEMs) and Focused Ion Beam Microscopes (FIBs) ), and has identified an**untapped source of information within the images normally made available to users: they have developed a**means to acquire and convey the "subtemporal" information contained within a standard pixel that is acquired**with the standard "dwell time" and one or more line averaging/integration passes. Dwell time and line**averaging settings are considered "standard" parameters to define a CPB scan, however allowing image**processing functions access to this subtemporal pixel information in a time and spatially resolved manner is a**new innovation, and is conveyed in a format referred to as "Tall, Wide SubPixels". In this project, we will**apply classification methods including Convolutional Neural Networks, Support Vector Machines and**statistical methods to characterize the SubPixel information and also look for correlations with other**multiple-sensor data, and design strategies to accelerate acquisitions based on real-time analysis of this**information.