Module Engineering - PhD Intern; Advanced Wet Etch and Cleans

The Role and Impact As a Module Engineering PhD Intern, you will contribute to the development and optimization of semiconductor manufacturing processes for a specific module. You will engage in sustaining and improving manufacturing equipment performance, troubleshooting issues, and supporting qualification activities. This internship offers an excellent opportunity to apply your academic research to practical engineering challenges, gain hands-on experience, and explore your future career potential in a fast-paced, innovation-driven environment.

Business group Intel Foundry is dedicated to advancing semiconductor manufacturing technologies and delivering innovative solutions that meet the needs of customers worldwide. As a part of this organization, you will join a team focused on optimizing manufacturing processes, sustaining equipment performance, and driving production excellence, while contributing to Intel's broader mission of technological leadership.

• Support the development and optimization of semiconductor manufacturing processes for a specific module.
• Sustain and improve manufacturing equipment performance to ensure operational reliability and efficiency.
• Assist in troubleshooting equipment issues and performing qualification activities to meet production standards.
• Collaborate with engineering teams to refine process control methodologies and achieve production goals.
• Apply advanced statistical analysis and experimental design to improve process reliability and throughput.
• Gain hands-on experience with materials and device characterization techniques, including SEM and TEM.
• Contribute to continuous improvement initiatives for process control systems and methodologies.

Must be actively pursuing a PhD degree in Materials Science Engineering, Physics, Chemistry, Electrochemistry or Chemical Engineering 3+ months of educational, research, laboratory, internship, or industry experience. Demonstrated hands-on experience through academic research, laboratory work, internships, coursework, or technical projects. Basic understanding of semiconductor manufacturing processes, process integration, or advanced manufacturing methodologies.

• Experience with data analysis, statistical methods, and process optimization techniques.
• Strong academic performance with a cumulative GPA of 3.0 or higher preferred.
• Demonstrated deep expertise in chemical reaction kinetics, selective wet etching, interfacial surface chemistry, corrosion/passivation mechanisms, or surfactant-assisted cleaning/drying mechanisms (e.g., Marangoni drying, supercritical CO2).
• Hands-on experience with advanced thin-film, surface, and chemical characterization techniques (e.g., Ellipsometry, AFM, SEM/TEM, XPS, contact angle goniometry, FTIR, TXRF, or ICP-MS).
• Experience with automated fluidic systems, wet processing instrumentation design, single-wafer spin hardware, or experimental fluid dynamics/mass-transfer troubleshooting.
• Demonstrated experience utilizing Python, MATLAB, JMP, or Machine Learning for design of experiments (DOE), multivariate statistical analysis, or process automation.
• Strong publication record, conference presentations, or filed patent applications demonstrating systematic problem-solving in experimental chemical and materials science.

• Experience with data analysis, statistical methods, and process optimization techniques.
• Strong academic performance with a cumulative GPA of 3.0 or higher preferred.
• Demonstrated deep expertise in chemical reaction kinetics, selective wet etching, interfacial surface chemistry, corrosion/passivation mechanisms, or surfactant-assisted cleaning/drying mechanisms (e.g., Marangoni drying, supercritical CO2).
• Hands-on experience with advanced thin-film, surface, and chemical characterization techniques (e.g., Ellipsometry, AFM, SEM/TEM, XPS, contact angle goniometry, FTIR, TXRF, or ICP-MS).
• Experience with automated fluidic systems, wet processing instrumentation design, single-wafer spin hardware, or experimental fluid dynamics/mass-transfer troubleshooting.
• Demonstrated experience utilizing Python, MATLAB, JMP, or Machine Learning for design of experiments (DOE), multivariate statistical analysis, or process automation.
• Strong publication record, conference presentations, or filed patent applications demonstrating systematic problem-solving in experimental chemical and materials science.

Job Type: Student /…