Product Engineer (Advance Data Analytics), Heterogeneous Integration Group (HIG), High Bandwidth Memory (HBM) Product Engineering
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Job Description
Key Responsibilities Product Disposition Solutions: Develop, validate and deploy product-disposition solutions (wormhole) to ensure quality and reduce Defects Per Million (DPM) through a comprehensive product disposition program/workflow.
Data Analysis for Yield Improvement and Reliability : Utilize in-house statistical tools, machine learning, and AI for engineering data analysis to enhance yields and reliability, integrating these improvements within the Dispo workflow.
Deviation Alert System: Create a deviation alert system to monitor yield loss, characteristic/parameter trends, and wafer/die/cube shading.
Models/Algorithm Development : Develop state-of-the-art algorithms, including Machine Learning and Deep Learning models, to advance data mining and pattern recognition for quality enhancement, yield improvement, wafer/die level screening and efficiency enhancement.
Mentorship and Development: Actively mentor and develop team members to foster growth and development within the team and the organization.
Cross-Functional Collaboration: Work closely with various cross-functional teams, including Fab, HBM Technology Development, HBM Design, System Development, and Quality/Reliability teams, to ensure the holistic development and successful shipping of end products.
Promotion of Innovation: Promote innovation and drive changes that provide a technical advantage over competitors, maintaining the company's competitive edge in the market.
AI/ML Advocate: Collaborate with cross-functional teams to develop, deploy, and validate AI/ML models aimed at enhancing key performance indicators (KPIs) such as Quality, Cost, Cycle Time, and Scale. AI Responsibilities:
• Integrates AI-assisted tools and insights into daily work to improve efficiency, quality, or effectiveness, exercising sound judgment and complying with organizational standards and legal requirements.
• Contributes to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements