ORCID Profile
0000-0003-2179-296X
Current Organisation
University of Oxford
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Publisher: IOP Publishing
Date: 09-2020
Abstract: Reconstructions in atom probe tomography (APT) are plagued by image distortions arising from changes in the specimen geometry throughout the experiment. The simplistic and inaccurate geometrical assumptions that underpin the conventional reconstruction approach account for much of this distortion. Here we extend our previous work of modelling APT experiments using level set methods to three dimensions (3D). This model is used to generate and subsequently reconstruct synthetic APT datasets from electron tomography (ET) of an A l - M g - S i multiphase specimen. Finally, we apply our model to the reconstruction of an experimental field-effect transistor (finFET) dataset. This model-driven reconstruction successfully reduces density distortions compared to conventional methods. By combining prior knowledge about the specimen geometry from sources such as ET, such an approach promises new distortion correcting APT reconstruction applicable to complex specimen geometries.
Publisher: IOP Publishing
Date: 07-2022
Abstract: Traditional reconstruction protocols in atom probe tomography frequently feature image distortions for multiphase materials, due to inaccurate geometric assumptions regarding specimen evolution. In this work, the authors’ outline a new reconstruction protocol capable of correcting for many of these distortions. This new method uses predictions from a previously developed physical model for specimen field evaporation. The application of this new model-driven approach to both an experimental semiconductor multilayer system and a fin field-effect transistor device (finFET) is considered. In both systems, a significant reduction in multiphase image distortions when using this new algorithm is clearly demonstrated. By being able to quantitatively compare model predictions with experiment, such a method could also be applied to testing and validating new developments in field evaporation theory.
Publisher: IOP Publishing
Date: 14-08-2019
Publisher: Cold Spring Harbor Laboratory
Date: 14-04-2020
DOI: 10.1101/2020.04.13.039420
Abstract: More than three decades ago, the microarray revolution brought about high-throughput data generation capability to biology and medicine. Subsequently, the emergence of massively parallel sequencing technologies led to many big-data initiatives such as the human genome project and the encyclopedia of DNA elements (ENCODE) project. These, in combination with cheaper, faster massively parallel DNA sequencing capabilities, have democratised multi-omic (genomic, transcriptomic, translatomic and epigenomic) data generation leading to a data deluge in bio-medicine. While some of these data-sets are trapped in inaccessible silos, the vast majority of these data-sets are stored in public data resources and controlled access data repositories, enabling their wider use (or misuse). Currently, most peer reviewed publications require the deposition of the data-set associated with a study under consideration in one of these public data repositories. However, clunky and difficult to use interfaces, subpar or incomplete annotation prevent discovering, searching and filtering of these multi-omic data and hinder their re-purposing in other use cases. In addition, the proliferation of multitude of different data repositories, with partially redundant storage of similar data are yet another obstacle to their continued usefulness. Similarly, interfaces where annotation is spread across multiple web pages, use of accession identifiers with ambiguous and multiple interpretations and lack of good curation make these data-sets difficult to use. We have produced SpiderSeqR, an R package, whose main features include the integration between NCBI GEO and SRA databases, enabling an integrated unified search of SRA and GEO data-sets and associated annotations, conversion between database accessions, as well as convenient filtering of results and saving past queries for future use. All of the above features aim to promote data reuse to facilitate making new discoveries and maximising the potential of existing data-sets. s-lab-cancerunit/SpiderSeqR
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Charles Fletcher.