Manufacturing Process Engineer, IRP - RDA

For more than 50 years, Photronics has been a global leader in photomask technology—enabling the innovation behind smartphones, computers, automotive electronics, and countless other devices used every day. Our success is driven by advanced technology, strong customer partnerships, and most importantly, our people.

Join Photronics and be part of a collaborative, high‑tech manufacturing environment where learning, engineering excellence, and continuous improvement are core to everything we do.

The IRP RDA Engineer is responsible for driving detection, classification, and reduction of all defect signals across the photomask manufacturing value stream—both traditional physical mask defects (e.g., extensions, intrusions, pattern defects) and non‑physical defects such as flow breakdowns, missing or ineffective OCAP, aged holds, disposition delays, and other systemic constraints that impact quality, yield, and cycle time.

This role owns the inspection–review–repair (IRP) workflow and leverages real‑time defect analysis (RDA) to identify root causes, drive containment and corrective actions, and improve process capability. The engineer partners with lithography, metrology, equipment, manufacturing, and planning/operations stakeholders to ensure stable production performance through data‑driven decision making and continuous improvement.

Photronics, Boise,  (US). This is an on‑site position, and we are not open to remote candidates at this time.

• Own and continuously improve the inspection–review–repair (IRP) workflow, ensuring fast, consistent disposition and effective closure of defect signals.
• Integrate and analyze diverse defect signals (inspection hits, review outcomes, repair results, holds, rework, OCAP usage, queue time) to identify trends, paretos, and excursion drivers.
• Develop and maintain real‑time monitoring (RDA) systems and dashboards for defect health, containment status, and flow performance (e.g., holds aging, disposition cycle time, IRP queue health).
• Lead root cause analysis and corrective/preventive actions for yield, quality, and flow excursions—spanning tool/process mechanisms, recipe/configuration, handling/contamination, and operational breakdowns.
• Partner with inspection, review, and metrology teams to improve signal quality (sensitivity, nuisance reduction, classification accuracy) and to standardize defect taxonomy across tools and workflows.
• Support rapid, high‑quality dispositions by evaluating impact (printability where applicable), driving clear containment actions, and reducing time‑to‑decision for holds, rework, and IRP exceptions.
• Support qualification and optimization of inspection, review, and repair tools, including recipe governance, matching, and sustaining plans tied to defect and flow performance.
• Apply SPC, DOE, and statistical methods to improve both process robustness and workflow robustness (e.g., defect escape reduction, rework reduction, queue‑time variation reduction).
• Drive OCAP and standard work effectiveness: identify gaps, author or improve response plans, ensure ownership/closure, and verify sustained control through audit‑ready evidence.
• Own recurring defect/flow review routines (e.g., pareto reviews, aged‑hold scrubs) and coordinate cross‑functional actions to eliminate chronic constraints in the back end of the photomask manufacturing process.
• Collaborate with process, equipment, manufacturing, and operations teams to improve yield, cycle time, and cost through prioritized, closed‑loop problem solving.
• Document defect mechanisms, control strategies, OCAP/standard work, and lessons learned; maintain clear knowledge bases that accelerate future disposition and root‑cause efforts.

• Hands‑on experience in photomask or semiconductor manufacturing environments, including working effectively in/with cleanroom operations.
• Working knowledge of photolithography/patterning and how upstream process variation can manifest as downstream inspection, review, repair, or disposition signals.
• Ability to transform high‑volume, mixed‑quality manufacturing data into actionable insights (paretos,…