Single Photon Source Lithography Engineer

About this position:

Role Overview We are looking for a Single Photon Source Lithography Engineer to support the development, stabilization and industrialization of our semiconductor fabrication processes. At our current stage of maturity, this position combines advanced process development, hands‑on R&D and manufacturing engineering. The Lithography Engineer will play a key role in bridging the gap between Advanced R&D and Production, transforming newly developed process building blocks into robust, repeatable and scalable manufacturing processes. The role will have a strong focus on in‑situ localization of quantum dots (QDs) at cryogenic temperatures and laser lithography over the localized QDs with laser writing techniques. The successful candidate will work closely with R&D teams, fabrication engineers, equipment owners and cleanroom operators to improve process capability, reproducibility, yield and production throughput. This is a highly hands‑on position requiring a strong understanding of semiconductor fabrication, rigorous experimental methodology and the ability to investigate complex interactions between process parameters and device performance.

Key Responsibilities

Process Development & Industrialization

• Develop, optimize and stabilize QD positioning and laser lithography process.
• Contribute to the development of GUIs and automation codes in the Python programming language.
• Contrinute to the development of fully-automated QD-positioning and litography tools.
• Validate and qualify new fabrication equipment and associated processes.
• Define process windows, recipes, operating conditions and acceptance criteria.
• Use structured experimentation, including Design of Experiments (DoE) where appropriate, to understand process sensitivities and optimize performance.
• Improve process repeatability, reproducibility, robustness and within-wafer / wafer-to-wafer homogeneity.
• Prepare processes and equipment for increasing wafer sizes, batch sizes and production volumes.

R&D to Production Transfer

• Act as a key technical interface between Advanced R&D and Production.
• Convert R&D process building blocks into stable, documented and manufacturable processes.
• Assess manufacturability, scalability and process risks before transfer to production.
• Define qualification plans and acceptance criteria before releasing new or modified processes to production.
• Lead or contribute to Process Failure Mode and Effects Analysis (PFMEA) for critical fabrication steps, identifying potential failure modes, their effects on device performance and appropriate prevention and control measures.
• Translate PFMEA outcomes into process controls, monitoring plans, acceptance criteria, Control Plans and documentation.
• Support technology transfer through clear documentation, operator training and technical communication.

Process Control & Yield Improvement

• Monitor and improve production process performance, yield and throughput.
• Establish regular process checks, controls and monitoring methodologies, including Statistical Process Control (SPC) where relevant.
• Detect process drifts, investigate deviations and define corrective and preventive actions.
• Lead root-cause analysis and troubleshooting activities for yield losses, process excursions and equipment-related issues.
• Define and maintain relevant process KPIs and contribute to continuous-improvement initiatives.
• Promote best practices that increase process stability, repeatability and equipment utilization.

Equipment & Production Support

• Provide hands‑on technical support to cleanroom operators and production teams.
• Work closely with equipment suppliers and internal teams to resolve technical issues.
• Contribute to a safe, disciplined and efficient cleanroom environment.

Process Characterization & Data Analysis

• Correlate process data with structural, optical and electrical characterization results.
• Use experimental data to identify process sensitivities, failure mechanisms and optimization opportunities.
• Develop appropriate data-analysis tools to support process monitoring and engineering decisions.
• Search and analyze scientific and technical literature to support process dev

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