Engineer, HIG-HBM PE, Media Health
5 days ago
Responsibilities:
- Cumulative Yield Ownership: Be a key member of cross-functional teams, to lead impactful initiatives that significantly enhance overall yield and quality improvement, driving substantial benefits for the organization.
- 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.
- 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.
- Product Disposition Solutions: Develop, validate and deploy product-disposition solutions to ensure quality and reduce Defects Per Million (DPM) through a comprehensive product disposition program/workflow.
- Tools/Dashboard/Deviation Alert System: Create tools/dashboard/deviation alert system to monitor yield loss, characteristic/parameter trends, and wafer/die/cube shading.
- Data Infrastructure Development: Collaborate with IT, Manufacturing, and Operations teams to develop and enhance the data infrastructure pipeline using cloud technologies to support reliability and engineering assessments.
- 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.
Requirements:
- Bachelors/Masters Electrical Engineering Degree with previous technical internships.
- Good knowledge of data analytics, data extraction and analysis tools.
- Proficient in scripting, coding and statistical analysis tool such as JMP, Tableau, Power BI will be an advantage.
- Dedicated and highly motivated with a flexible approach towards adapting to different roles in a dynamic working environment.
- Keen in collaborative work within/across teams to address sophisticated business and engineering problems.
- Ability to effectively articulate technical challenges and solutions
- Strong sense of responsibility and accountability towards assigned role with professional work ethic.
- Work on site in Singapore with international travel to USA, Taiwan and Japan.
FMEA
Product Design
Technology Development
Physics
Mentorship
Project Management
CMOS
Simulations
Product Development
Decision Making
Verilog
Manufacturing
CAD
Electronics
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