Fanzhi Su

DecurtainNet, A Deep Learning Model and Large-Scale Dataset for Fast Curtaining Artefact Feature Removal in Electron Microscopy

Fanzhi Su, May Ching Lai, Tianhao Wu, wei Huang et autres

Focused Ion Beam (FIB) milling enables electron microscopy (EM) studies of mate rial across diverse applications. However, the use of FIB on composite or beam-sensitive materials can cause curtaining artefacts compromising EM data quality. Existing mit igation approaches, such as optimised FIB …

DecurtainNet Dataset

Fanzhi Su, Tianhao Wu, Wei Huang, Jedrzej Morzy et autres

DecurtainNet Dataset is an extensive collection of over 22,000 electron microscopy (EM) images meticulously curated to develop and validate DecurtainNet, a UNet-based deep learning model aimed at automatically removing curtaining artefact revealed in EM images. Curtaining artefacts, resulting from uneven milling rates …

DecurtainNet Dataset

Fanzhi Su, Tianhao Wu, Wei Huang, Jedrzej Morzy et autres

DecurtainNet Dataset is an extensive collection of over 22,000 electron microscopy (EM) images meticulously curated to develop and validate DecurtainNet, a UNet-based deep learning model aimed at automatically removing curtaining artefact revealed in EM images. Curtaining artefacts, resulting from uneven milling rates …

Machine Learning-Enabled Characterisation of Energy Materials and Devices via Electron Microscopy

Fanzhi Su

The development of next-generation energy storage and optoelectronic devices, including lithium-ion batteries, perovskite solar cells, and light-emitting diodes (LEDs), relies on the ability to design materials with tailored structures and properties. A central challenge in materials science is to establish the link …

Deep Learning-Assisted Multivariate Analysis for Nanoscale Characterization of Heterogeneous Beam-Sensitive Materials

Felix Utama Kosasih, Fanzhi Su, Tian Du, Sinclair R. Ratnasingham et autres

Nanoscale materials characterization often uses highly energetic probes which can rapidly damage beam-sensitive materials, such as hybrid organic-inorganic compounds. Reducing the probe dose minimizes the damage, but often at the cost of lower signal-to-noise ratio (SNR) in the acquired data. This work …

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