As a Staff Test Process Engineer, you will take ownership and provide leadership in the Known Good Die (KGD) Process and Burn In (BI) Test Process. You will lead cross-functional projects to improve test process robustness, scalability, and efficiency, while supporting execution and resolving manufacturing issues.
Your role will focus on driving continuous improvement initiatives, deploying systematic methodologies and tools, and ensuring best-in-class standards are applied in semiconductor testing.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
- Lead KGD and BI test process improvement projects, ensuring timely and successful execution.
- Oversee and enhance wafer sorting, wafer level testing, and burn in test processes to ensure yield, quality, and reliability.
- Partner with cross functional team to scope, plan, design, and sustain test processes.
- Support and troubleshoot manufacturing test process issues; propose and implement effective corrective and preventive actions.
- Drive cost-saving initiatives by working closely with multiple departments.
- Develop, document, and maintain test cases, test plans, and process documentation (WI, SOP, Control Plans).
- Define clear goals and success criteria for process tests; establish systematic steps for execution.
- Apply structured problem-solving methodologies (FMEA, 8D, root-cause analysis) to improve test yield, reliability, and efficiency.
- Provide technical coaching and mentoring to junior engineers and technicians to enhance team capabilities.
- Ensure high engineering standards, compliance, and quality throughout all KGD and BI processes.
- Leverage analytical tools and data-driven approaches to improve test systems and methodologies.
- Identify potential risks, implement monitoring systems, and ensure proactive quality control.
- Collaborate in new product introduction (NPI) to ensure smooth transition from development to high-volume manufacturing.
- Collaborate with cross-site teams to drive standardization and best-practice sharing.
- Accountable for reducing cost through process optimization, hardware efficiency, or automation improvements.