Drive root cause investigations by correlating electrical test data, FA results, process information, manufacturing history, and AI-generated insights to identify yield detractors and recommend corrective actions.
Develop and maintain yield monitoring methodologies, screening strategies, defect classification systems, and AI-assisted detection models to improve excursion response and defect identification accuracy.
Partner with Design, Process Integration, Product Engineering, Test, Manufacturing, and Data Science teams to resolve yield and reliability issues and drive product quality improvements.
Develop, automate, and maintain data analysis tools, dashboards, reporting systems, and AI-powered workflows to improve investigation efficiency and reduce manual data processing.
Apply AI, machine learning, statistical methods, and advanced analytics techniques to accelerate defect identification, yield trending, anomaly detection, predictive analysis, and root cause determination.
Leverage generative AI and knowledge-management tools to accelerate technical reviews, investigation planning, report generation, and information retrieval.
Lead technical reviews and communicate investigation results, risks, recommendations, and data-driven insights to cross-functional stakeholders and management.
Create and maintain technical documentation, best practices, training materials, AI prompt libraries, and knowledge-sharing resources for the organization.
Evaluate and implement new electrical characterization, debug methodologies, automation solutions, and AI-enabled FA techniques to improve problem-solving capability.
Support new product introduction (NPI), qualification activities, and technology transfers by providing yield analysis, failure investigation expertise, and data-driven risk assessments.
Identify opportunities for process improvement, cost reduction, cycle-time reduction, and productivity enhancement through automation, AI adoption, and workflow optimization.
Provide technical mentorship and training to engineers and technicians on semiconductor device operation, failure mechanisms, data analytics, AI-assisted engineering tools, and debug methodologies.
Champion AI-enabled ways of working by identifying, evaluating, and implementing emerging technologies that improve engineering productivity, technical insight generation, and decision-making.
Manage and prioritize multiple investigations simultaneously while ensuring timely communication of critical findings and business-impacting issues.
Minimum Qualifications
Bachelor's or master's degree in Electrical Engineering, Computer Engineering, Materials Science, Computer Science, Data Science, or Chemical Engineering.
Ability to apply knowledge of semiconductor devices, process integration, memory architecture, and device operation to analyze electrical failures and yield issues.
Ability to perform electrical and failure analysis as defined by area skill guidelines.
Experience conducting advanced data analysis, statistical evaluation, and data visualization to support yield investigations and failure analysis results.
Proficiency with data analytics, scripting, automation, or programming tools (e.g., Python, SQL, JMP, Power BI, Tableau, or equivalent).
Ability to leverage AI tools, large language models, and automation technologies to improve data processing, engineering productivity, investigation efficiency, and technical reporting.
Ability to summarize investigation findings, including potential root cause and recommended actions, in a concise and technically sound report.
Ability to operate lab equipment safely and perform required tasks and basic troubleshooting.
Preferred Qualifications
Experience applying AI, machine learning, predictive analytics, or large language models to engineering, failure analysis, manufacturing, or yield improvement workflows.
Experience developing automated data processing pipelines, dashboards, engineering applications, or workflow automation solutions.
Experience mentoring or assisting failure analysis technicians during yield investigations.
Familiarity with Merlin/Raptor tester platforms, emission tools, MPI probe stations, nanoprobe tools, and associated data analysis environments.
Experience maintaining technical knowledge of semiconductor device structure, operation, and failure mechanisms for current and future technologies.
Demonstrated participation in digital transformation, AI adoption, continuous improvement initiatives, or cross-functional technical projects.
Willingness to evaluate emerging AI technologies and drive adoption of innovative engineering solutions that improve team effectiveness and productivity.
As a world leader in the semiconductor industry, Micron is dedicated to your personal wellbeing and professional growth. Micron benefits are designed to help you stay well, provide peace of mind and help you prepare for the future. We offer a choice of medical, dental and vision plans in all locations enabling team members to select the plans that best meet their family healthcare needs and budget. Micron also provides benefit programs that help protect your income if you are unable to work due to illness or injury, and paid family leave. Additionally, Micron benefits include a robust paid time-off program and paid holidays. For additional information regarding the Benefit programs available, please see the Benefits Guide posted on micron.com/careers/benefits.
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