Jabeen Abbasi

Lithography module engineer specializing in EUV materials, track platform development, tool qualification, and process development as a part of the team responsible for implementing advance semiconductor nodes in high-volume manufacturing (HVM).

• Spearheaded two new EUV and one High NA EUV track tools qualifications, collaborating with external suppliers to optimize tool performance and ensure readiness for HVM with safety, quality, cost indicator goals, and zero delays.
• Led development and qualification of lithography processes for Intel’s 20A, 18A, 14A and beyond technology nodes, enabling next-generation logic technologies for foundry customers through precision patterning and process control.
• Optimized and successfully stabilized new metal oxide resist (MOR) EUV lithography platform by identifying bottleneck in underlayers, thin film deposition, lithography, develop, and etch stages. Implemented streamlined solutions improving critical dimension (CD) uniformity, reducing line edge roughness (LER), improve etch selectivity, and enhance overall stability for sub-28nm and below patterning in HVM.
• Owned tool performance monitoring and data analysis to proactively identify and resolve module issues, preventing process excursions. Independently operated advanced metrology tools and developed CD-SEM recipes to measure the patterning features.
• Recognized in lithography module forum for leading root cause identification and restoring process stability in HVM following a first-of-a-kind Plasma etch Dry Develop tool upgrade, resolving baseline shift and preventing downstream yield impact.
• Improved process optimization efficiency under wafer and tool time constraints by leveraging JMP and fractillia software for complex data analysis and targeted Design of Experiments (DOE) across key modules. Reduced DOE volume by 30%, cut cycle time by 20%, and saved 100+ wafer hours per quarter. Collaborated with cross-functional internal teams and external suppliers to drive corrective actions, enhancing process robustness and stability across EUV and High-NA lithography platforms.
• Executed New Product Introduction (NPI) by aligning technical roadmaps with business objectives and design specifications; developed and managed Gantt charts to coordinate cross-functional teams, achieving 100% on-time delivery and a significant reduction in time-to-qualification for critical process milestones.
• Developed and implemented standard operating procedures (SOP) to streamline data handling in manufacturing processes, significantly reducing turnaround time by 35% and enhancing process efficiency to 25% for faster data-driven decision-making.
• Provided technical mentorship to over 10 newly hired engineers, fostering a culture of continuous learning, collaboration, and efficiency in a fast-paced semiconductor environment. Spearheaded team-building initiatives to enhance engagement and performance. Served on the Lithography Module hiring panel, contributing to strategic talent acquisition and team growth.

• Designed and built two cost-effective lab scale thin film deposition systems enabling scalable fabrication of three magnetic chalcogenides system for spintronic applications.
• Engineered precise processing conditions for thin film deposition of novel magnetic chalcogenide systems, achieving >95% phase purity in high-crystallinity thin films. Characterized structural and electronic properties using advanced techniques to validate performance and suitability for device integration.
• Resolved instrumentation issues and optimized analytical tools for thin film characterization, establishing robust sampling protocols that improved data acquisition efficiency and accelerated low-cost material discovery by 30%, enabling faster evaluation of novel magnetic materials.
• Collaborated with cross-functional teams to design and execute experiments across diverse research domains. Delivered comprehensive technical reports, identified critical issues, and proposed actionable solutions to drive project success and innovation.
• Performed advanced computational simulations and data analysis to quantify structural changes in doped materials, enabling predictive modeling of material behavior and aligning theoretical frameworks with experimental results.