A Smart Automated TEM Facility for Large Scale Analysis of Atomic Structure and Chemistry
Lead Research Organisation:
University of Manchester
Department Name: Materials
Abstract
Advanced materials lie at the heart of a huge number of key modern technologies, from aerospace and automotive industries, to semiconductors through to surgical implants. The transmission electron microscope (TEM) is a key enabling technology for advanced material research because it offers two important pieces of atomic information: firstly the location of atoms can be determined from studies of elastic scattering of electrons by the sample, and secondly the chemical composition of atomic sites within the materials structure can be recovered from spectroscopic studies of the inelastic transfer of energy to the sample (either from direct energy loss or by the detection of characteristic X-rays). These two pieces of information underpin a huge research area exploring the relationship between materials microscopic structure and the macroscopic properties it exhibits. With the drive towards nanotechnologies and quantum devices the ability to discover the most precise understanding of individual atoms is an essential capability for facilities supporting research of advanced materials.
The aim of the project is to develop, for the first time, an analytical TEM that not only offers cutting edge spectroscopy performance but which also is designed with artificial intelligence and automated workflows at its core. The first goal will be achieved through the incorporation of the latest generation of X-ray detectors and spectrometers to provide order of magnitude improvements in chemical sensitivity and precision. This capability is essential for the move to studying devices as small as a single atomic defect as well as for efficient analysis of large areas at atomic resolution.
To achieve artificial intelligence (AI)-assisted experiments the project will tackle a number of technical challenges:
i. Computer control of the TEM will be developed, allowing the computer to automatically adjust the sample stage and beam to address specific regions of interest and perform auto-tuning the experimental parameters to achieve detailed high resolution imaging and diffraction based analysis of nanometric regions without the need for continuous operator interaction.
ii. The mechanism to identify regions of interest will utilise the full range of machine learning (ML) approaches to segment lower resolution data, which might come from fast large-area scanning in the TEM or be the result of ex-situ analysis by optical imaging, scanning probe microscopies, scanning electron microscopy or optical approaches to name but a few.
iii AI training will allow the microscope control computer to build functional relationships between experimental results in the same way a human operator does, allowing faster and more systematic identification of novel features.
Our proposed new smart automated TEM (AutomaTEM) offers the opportunity to gain at least an order of magnitude increase in the volume of data that is readily accessible through automated workflow analysis. Features of interest will be determined either through user-defined parameters or through the AI identification of significant features in the collective data. This will allow meaningful statistics to be gathered about the size, shape, atomic structure, composition, electronic behaviour of potentially hundreds or thousands of regions in a given sample. This in turn will enable more complete understanding of nanostructure heterogeneity and structure-property relationships in materials.
The aim of the project is to develop, for the first time, an analytical TEM that not only offers cutting edge spectroscopy performance but which also is designed with artificial intelligence and automated workflows at its core. The first goal will be achieved through the incorporation of the latest generation of X-ray detectors and spectrometers to provide order of magnitude improvements in chemical sensitivity and precision. This capability is essential for the move to studying devices as small as a single atomic defect as well as for efficient analysis of large areas at atomic resolution.
To achieve artificial intelligence (AI)-assisted experiments the project will tackle a number of technical challenges:
i. Computer control of the TEM will be developed, allowing the computer to automatically adjust the sample stage and beam to address specific regions of interest and perform auto-tuning the experimental parameters to achieve detailed high resolution imaging and diffraction based analysis of nanometric regions without the need for continuous operator interaction.
ii. The mechanism to identify regions of interest will utilise the full range of machine learning (ML) approaches to segment lower resolution data, which might come from fast large-area scanning in the TEM or be the result of ex-situ analysis by optical imaging, scanning probe microscopies, scanning electron microscopy or optical approaches to name but a few.
iii AI training will allow the microscope control computer to build functional relationships between experimental results in the same way a human operator does, allowing faster and more systematic identification of novel features.
Our proposed new smart automated TEM (AutomaTEM) offers the opportunity to gain at least an order of magnitude increase in the volume of data that is readily accessible through automated workflow analysis. Features of interest will be determined either through user-defined parameters or through the AI identification of significant features in the collective data. This will allow meaningful statistics to be gathered about the size, shape, atomic structure, composition, electronic behaviour of potentially hundreds or thousands of regions in a given sample. This in turn will enable more complete understanding of nanostructure heterogeneity and structure-property relationships in materials.
