Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centered experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for the edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows and the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.Comment: Review Articl

Scanning Electron Microscopy for Nano-morphology Characterisation of Complex Hierarchical Polymer Structures

This thesis presents novel and innovative ways of imaging and analysing natural hierarchical polymers using low-voltage scanning electron microscopy and secondary electron energies. Materials such as plant fibres, feathers and silk, have received increased societal and scientific interest recently, while the plastic industry is faced with growing public concerns over its generation of waste, and use of petrochemical precursors. In nature, materials are produced sustainably and they furthermore exhibit inspiring mechanical performance. One such material is spider silk, which is spun at room temperature from a water-based protein gel to form a thread, which even with diameters as small as 5 μm easily suspends the weight of a palm-sized spider. It is known that the secret to spider silk’s remarkable properties lies within its nanoscale structures. However, the direct observation of these nanostructures has remained difficult due to their small size and their sensitivity to chemical and mechanical alteration. This work presents novel sample preparation protocols and demonstrates their use in accessing size and location information of key nanostructures within spider silk through nanoscale observation in the scanning electron microscope. As the secondary electron spectroscopy technique employed here is relatively new, new workflows from sample preparation, over optimal imaging and spectral acquisition and novel multivariate data analysis techniques are innovated and described in detail. The rigorous consideration of the material and method are exemplified on a feather section, to show that the secondary electron energy signal in the scanning electron microscope may generate molecular composition maps on a proteinaceous structural polymer. This work lays out all requirements for unlocking the vast potential for nanoscale chemical mapping which lies in the nanoscale secondary electron signal, to further inspire ground-breaking studies into the nanostructures of complex hierarchical polymers

Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

A Study of Fabrication of Ultra-high Resolution Nano Devices through Electron Beam Lithography Process and Its Application to Electron – Optical Systems

Today’s semiconductor industry has been significantly changing in its techniques and processes for the fabrication of devices and accordingly, there has been dramatic increase in performance and a reduction in cost. To obtain still higher device performances and still further cost reduction, the dimensions of patterns in integrated circuits should be as small as possible and the 3-dimensional accuracy of multidimensional semiconductor structures should be also achieved as well. The manufacturing of smaller feature dimensions and 3-dimensional devices has been enabled by developments in lithography – the technology which transfers designed patterns onto the silicon wafer. Especially, electron beam lithography is widely adapted in the nano fabrication technology due to its ability to achieve nanometer-scale resolution. The aim of this work is to fabricate test devices by the electron beam lithography possesses and apply them to the test of electron optical systems. In this thesis, we first develop methods to fabricate a high resolution nano scale Fresnel zone plate and 3-dimenstional stair case structure by E-beam lithography. To optimize the fabrication we optimized the lithographic process and the subsequent process steps accounted for proximity effects via a correction program and controlled pattern transfer through reactive ion etching (RIE). The completed devices were tested in a Scanning Electron Microscopy (SEM) and the accuracy of feature parameters were examined by Fast Fourier Transformation methods (FFT). Finally, the application of these structures to the calibration and testing of e-beam systems was explored