Advanced image analysis of semiconductor structures

This project builds on diffraction imaging technology we introduced in EPSRC grant EP/P015719/1 for a scanning electron microscopy technique (SEM), so called "electron channelling contrast imaging" (ECCI). The tools we will develop are also applicable for electron backscatter diffraction (EBSD), another non-destructive technique, but one which is better at measuring and quantifying material deformations.

EBSD is commonly applied in metallurgy (for example at Strathclyde's advanced forming centre to examine welds), but within the Physics department, we deploy it for measuring semiconductor material properties, including recently demonstrating that ECCI-like data can also be obtained by this technique. This leads to large quantities of data which encodes information on the morphology, stress and strains, plus the defects that either arise from or produce strain.

Interpreting this flood of data is challenging, and while for ECCI we have made progress in finding and identifying extended defects, these are only part of the story. What is needed is an understanding of the meso-scale structure of these materials, which not only requires interpreting structure and strain on a larger scale, but also unpicking the influences which produce these deformations and features.

Deep learning image analysis (a core area of current AI research) can rapidly, with reasonable accuracy, pick out features in images, including locating areas of interest for various forms of microscopy. In this project, not only defects, but also regions with net changes in orientation due to the defects (so called sub-grains) will be found and studied in a largely automated way.

In metallurgy, these changes are usually ascribed to 'geometrically necessary dislocations', i.e., net shift in crystal orientation because of defects.

However, this concept misses contributions which cancel each other out (for example lines of dislocation with alternating Burgers' vectors) and does not explain how these structures arise (i.e. what is happening below the surface and during the growth history of the material to produce this pattern).

There is also a wealth of other data available in the SEM (light emission, induced electrical current, x-ray emission, etc.) which correlates with this surface microstructure as well as revealing information from deeper in the material, but which is only occasionally compared against the structural data. The surface morphology, plus information from these other messenger sources, combined with information from samples grown to different thicknesses or thinned after growth enables modelling of how the surface resulted from deeper processes and conditions (growth front coalescence, dislocation annihilation, substrate miscut, ...).

This is where other forms of machine learning (ML) and AI can be deployed to gain understanding (not just description) of these materials. By combining generative models and active learning, we can in the second stage of the project move to both simulating the material growth, but more importantly use 'counterfactual AI' to explore the influences that led to the measured data.

The counterfactual approaches are normally used in methods to produce explainable models (i.e. avoiding 'black-box' behaviour of ML), but can be deployed to trace the impact and interaction of model inputs and processes. Here, various 'what if?' models are run to disentangle the core influences and predict key variables for the resulting state of the material.