As part of the HBM Product Development Failure Analysis (PDFA) team, you will play a meaningful role in bringing next-generation HBM products from design through qualification and into high production manufacturing. The team focuses on ensuring product reliability, accelerating time-to-market, and enabling rapid production ramp by identifying and resolving component-level failures during new product introduction phases.

In this role, you will apply electrical, physical, and statistical failure analysis techniques to characterize failures, identify root causes, and drive corrective actions. You will collaborate with multi-functional engineering teams and leverage advanced analytics, statistical methods, and AI-enabled tools to improve failure detection, yield, reliability, and overall product performance.

Responsibilities

• Perform electrical, physical, and statistical failure analysis to identify, characterize, and understand defect and failure mechanisms in HBM products.

• Analyze component- and system-level data to resolve root causes of failures using statistical techniques, engineering methodologies, and AI-enabled tools.

• Apply AI tools (e.g., Copilot, Claude) and improved analytics to enhance failure detection, reliability learning cycles, yield improvement, and component performance.

• Work closely with Product, Process, Test, Quality, and Manufacturing Engineering teams to resolve fabrication, assembly, and test-related issues.

• Identify process defects, assembly-related failures, and test flow marginalities through detailed data analysis and characterization.

• Communicate technical findings, recommendations, and corrective actions to team members and drive issue resolution across multi-functional teams.

• Support rapid production ramp, product qualification, and continuous reliability improvement initiatives.

Minimum Qualifications

• Bachelor’s degree in Electrical Engineering, Computer Engineering, Physics, Materials Science, or a related field.

• 2+ years of experience performing electrical and statistical failure analysis to identify and understand failure mechanisms.

• 2+ years of experience applying electrical and physical characterization techniques for semiconductor failure analysis.

• Demonstrated analytical and problem-solving skills, including the application of statistical methods and tools for data analysis.

• Experience using AI, machine learning, or data analytics tools to support engineering analysis and root cause investigations.

• Coursework, training, or practical experience in one or more of the following areas: VLSI, semiconductor device physics, semiconductor processing, Python programming, and statistics.

• Strong written and verbal communication skills with the ability to collaborate effectively in multi-functional engineering teams.

Preferred Qualifications

• 5+ years of experience performing electrical and statistical failure analysis in the semiconductor industry.

• 5+ years of experience applying electrical and physical characterization techniques for semiconductor device and component analysis.

• Advanced coursework or hands-on experience in VLSI design, semiconductor processes, device physics, Python, and applied statistics.

• Experience using Artificial Intelligence or Generative AI tools (e.g., Copilot, Claude) for large-scale data analysis, failure prediction, and root cause identification.

• Experience using statistical analysis software such as JMP.

• Confirmed ability to influence technical outcomes and drive issue resolution in a multidisciplinary engineering environment.

• Proven ability to communicate complex technical findings to diverse team members and lead multi-functional collaboration efforts.