Keynote Speaker
Dr Jack Alexander-Webber
High-throughput correlated microscopy and automated device integration of 1D and 2D materials
The development of novel devices based on low-dimensional materials is often hindered by the slow, labour-intensive nature of nanoscale characterisation and device integration. To address this critical bottleneck, we present a high-throughput methodology that integrates multi-modal correlated microscopy with computer vision to create a fully automated workflow from material identification to device integration. Central to our approach is an optimised machine-readable, multi-scale fiducial marker system [1] that enables robust, nanometer-precision image registration across diverse microscopy platforms, including optical, scanning electron (SEM), and atomic force microscopy. This framework is coupled with advanced optical imaging techniques, such as Spectroscopic Ellipsometric Contrast Microscopy (SECM), for the rapid, non-destructive, and large-area mapping of material properties. We generate and process the resulting datasets to identify, classify, and extract key parameters from individual nanostructures, such as individual semiconductor nanowires or 2D heterojunctions.
Here, we demonstrate the identification and categorisation of structural variations InAs nanowires with high precision. Our method combines high-throughput optical imaging with automated site-selective high-resolution SEM to conduct a comprehensive statistical analysis of thousands of individual nanowires. When this is combined automated device design and fabrication of hundreds of single-nanowire devices we can statistically determine inter-nanowire variability and relate this to different growth conditions. For 2D materials, we build on the capabilities of imaging ellipsometry to rapidly map twist-angle disorder in twisted bilayer graphene [2] and to precisely locate the interfaces in laterally grown MoSe₂–WSe₂ heterostructures [3], enabling their targeted integration into functional optoelectronic devices. By transforming nanoscale device prototyping into a scalable, data-intensive process, our methodology accelerates the feedback loop between material synthesis, characterisation, and device performance, for the rapid development and application of nanostructured materials.
References:
[1] Potočnik, et al. ACS Nano 16, 18009 [2022]
[2] Potočnik, et al. Nano Letters 23, 5506 [2023]
[3] Potočnik, et al. Small Methods, 10.1002/smtd.202500437 [2025]