Fab Sort Manufacturing (FSM) is responsible for the production of all Intel silicon using some of the world's most advanced manufacturing processes in fabs in Arizona, Ireland, Israel, Oregon and 2 new greenfield sites in Ohio and Germany.As part of Intel's IDM2.0 strategy, FSM is rapidly expanding its operation to deliver output for both internal and foundry customers with state-of-the-art technologies arriving in high-volume manufacturing at a 2-year cadence going forward.Intel recently created HVM Global Yield organization in FSM to strengthen its yield operation and enable fast-paced yield ramp-up in early HVM phases for each technology in collaboration with Technology Development team and FSM fab managers.This job requisition is to seek Data Science (DS) team engineering roles in FSM HVM Global Yield organization, reporting to Data Science team manager.Selected candidates will work with other members in Global Yield org including Process Integration, Device and Defect engineering teams, fab module/yield teams and TD team members to achieve yield ramp-up and process optimization in early production stage, supporting internal and external customers.Data Science engineers' responsibilities include (but are not limited to):Identify valuable data sources and set automated data collection process to build and manage the dataset used for yield analysis.Processing of structured and unstructured data, and building predictive models and machine learning algorithms.Work with Yield Modelling team to develop new yield analysis methods and algorithms to deliver world class yield analytics and machine-learning solutions in high-volume manufacturing environment.Collaborate with Technology Development, Program Managers, Process Integration, Device Integration and Defect teams to identify yield and performance detractors and enhancement opportunities and support fast paced yield ramp-up in high-volume manufacturing phase.Support development of models to predict production line yield accurately in early stage of Si progression and deliver proposals to benefit production yield.Develop model and algorithms to improve and automate process control minimizing yield loss associated with tight process window.Engineering support for technical interactions with internal and external customers.Candidate should possess the following behavioral skills:Problem-solving technique with strong self-initiative and self-learning capabilities.Ability to work with multi-functional, multi-cultural teams.Must demonstrate solid communication skills.QualificationsMinimum Qualifications:Bachelor's degree in science and engineering major.3+ years' experience in advanced node semiconductor industry in yield analysis and data science.3+ years' experience in program languages and big data to develop a new analysis method and algorithms using large amount of fab data. Expertise in big data analysis and machine-learning.3+ years' experience in Device Physics and overall FinFET process flow.PreferredQualificationsAdvanced degree (Master's or Ph.D.) in science and engineering major.Experience in working or leading a TFT project.Experience in serving external Foundry customers through technical interactions.Demonstrated interpersonal skills including influencing, engaging, and motivating.Experience in new semiconductor technology development.Basic understanding on module processes including lithography, dry etch, wet etch, CMP, diffusion, implant, thin films and metrology.
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