Overview
Semiconductor fabrication is one of the most complex and precision-driven forms of manufacturing. At nanometre scales, even subtle variations in process conditions can introduce defects that degrade device performance, reduce yield, and drive up production costs. This project aims to develop advanced deep learning models capable of predicting fabrication outcomes and guiding fabrication recipe optimisation. By learning directly from experimental and process data, these models will enable a shift from iterative, trial-and-error fabrication towards predictive and data-driven manufacturing.
Responsibilities
- Develop advanced deep learning models capable of predicting post-fabrication device characteristics from process parameters.
- Engage with complex, high-dimensional datasets derived from real fabrication workflows, including microscopy, spectroscopy, and electrical performance measurements.
- Collaborate with fabrication engineers to translate physical processes into machine learning models; design, train, and evaluate deep learning architectures; assess generalisation across different process conditions.
- Validate models experimentally and test at industrial scale in collaboration with global companies in semiconductor fabrication and electronic design automation.
- Supervise PhD students and junior researchers; contribute to shaping the research direction of the team.
- Contribute to development of innovative technologies with a clear pathway to commercialisation through the spinout company Deep Fabrication.
Qualifications / Role details
The position offers a rare opportunity to apply machine learning to an important technical challenge with substantial potential impact. The role will provide deep exposure to nanofabrication processes, experience with industry-relevant datasets and problems, and opportunities to publish in leading journals and conferences. It is particularly well-suited to candidates who are motivated by applying machine learning to real-world systems where the underlying physics is complex and not fully understood.
We are committed to equality, diversity and inclusion and welcome applicants who support our mission of inclusivity.
Duration
This position is offered for 24 months in the first instance, with the possibility of extension for a further 12 months.
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Semiconductor fabrication is one of the most complex and precision-driven forms of manufacturing. At nanometre scales, even subtle variations in process conditions can introduce defects that degrade device performance, reduce yield, and drive up production costs. This project aims to develop advanced deep learning models capable of predicting fabrication outcomes and guiding fabrication recipe optimisation. By learning directly from experimental and process data, these models will enable a shift from iterative, trial-and-error fabrication towards predictive and data-driven manufacturing.
Responsibilities
- Develop advanced deep learning models capable of predicting post-fabrication device characteristics from process parameters.
- Engage with complex, high-dimensional datasets derived from real fabrication workflows, including microscopy, spectroscopy, and electrical performance measurements.
- Collaborate with fabrication engineers to translate physical processes into machine learning models; design, train, and evaluate deep learning architectures; assess generalisation across different process conditions.
- Validate models experimentally and test at industrial scale in collaboration with global companies in semiconductor fabrication and electronic design automation.
- Supervise PhD students and junior researchers; contribute to shaping the research direction of the team.
- Contribute to development of innovative technologies with a clear pathway to commercialisation through the spinout company Deep Fabrication.
Qualifications / Role details
The position offers a rare opportunity to apply machine learning to an important technical challenge with substantial potential impact. The role will provide deep exposure to nanofabrication processes, experience with industry-relevant datasets and problems, and opportunities to publish in leading journals and conferences. It is particularly well-suited to candidates who are motivated by applying machine learning to real-world systems where the underlying physics is complex and not fully understood.
We are committed to equality, diversity and inclusion and welcome applicants who support our mission of inclusivity.
Duration
This position is offered for 24 months in the first instance, with the possibility of extension for a further 12 months.
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