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Machine learning to predict the environmental footprint of semiconductor processes

/ Machine learning to predict the environmental footprint of semiconductor processes Machine learning to predict the environmental footprint of semiconductor processes Master internship - Leuven | More than two weeks ago Use unsupervised machine learning to help understand the environmental impact of semiconductor manufacturing The semiconductor industry is not only growing rapidly, butits manufacturing processes are increasing in complexity, requiring more andmore energy, water, and materials. With those natural resources already understrain, to remain compatible with planetary boundaries (e.g., to align with thedrastic emissions reduction required to mitigate climate change), it has becomecritical for technological advancement to integrate environmentalsustainability “by design”. To tackle this challenge, the SSTS program at imecis working along with partners from across the value chain to assess andimprove the sustainability of the semiconductor industry. The central tool to this endeavour is a “virtual fab” modelthat simulates high-volume manufacturing semiconductor fabs with all therelated flows and their environmental impacts (accessible through the imec.netzero webapp). This modelrequires accurate data on the tools and processes used in semiconductormanufacturing (e.g., required energy, processing time, chemicals, ...). However,due to confidentiality concerns this data is not always readily available orshareable. Objective This internship will apply machine learning techniques to tackletwo related data sharing issues in the context of semiconductor manufacturing sustainabilityassessment: - First, defining a model that can predict missingvalues in SSTS databases. Typically, this would involve predicting the flow of a given chemical of a tool, given the chemical flows of similar chemicals andtools as well as the specific data available for the target tool, for example using(sparse) nonnegative matrix factorization (NMF). - Second, aggregating tool and processes databasesinto a representative set of “generic tools/processes” that can be shared withoutconfidentiality concerns, by relying on privacy-aware unsupervised machinelearning tools. An extension of the models (e.g. NMF) from the first objectivemight be highly relevant. Responsibilities You will actively engage in the research and modelling of a few selected mitigation options, and implementing and testing those models intothe imec.netzero virtual fab code. This will involve working closely with both ourresearch team to understand the nuances of semiconductor manufacturingprocesses and life-cycle assessment methodology, as well as with the codingteam to ensure seamless integration of your models within the imec.netzerosystem. Your contribution will greatly enhance the environmental impactreduction roadmaps of semiconductor production. Skills and Learning Objectives: Applicants are expected to have a background in appliedmathematics with some experience in “traditional” machine learning*, and havesolid experience in Python programming. They have the desire to gainproficiency and enhance their skills in the following key areas: - Unsupervised ML, privacy-aware ML, nonnegativematrix factorization - Semiconductor Manufacturing Processes - Professional Software Engineering (architecture,documentation, clean code) *Here “traditional machine learning” refers to ML approachesbesides deep neural networks, which would be unsuitable in this contextdue to a lack of large training datasets. Type of project: Internship, Thesis, Combination of internship and thesis Required degree: Master of Engineering Technology, Master of Engineering Science, Master of Science Supervising scientist(s): For further information or for application, please contact: Vincent Schellekens (Vincent.Schellekens@imec.be) imec's cleanroom #J-18808-Ljbffr

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Sustainable ML for Semiconductor Process Footprint
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  • 21 februari 2026