Module Engineering - PhD Intern (Advanced Wet Etch and Cleans)

Intel•Hillsboro, OR

3d•Onsite

About The Position

As a Module Engineering PhD Intern, you will contribute to developing and optimizing semiconductor manufacturing processes for a specific module. You will help sustain and improve manufacturing equipment performance, troubleshoot issues, and support qualification activities. The internship provides an opportunity to apply academic research to practical engineering challenges, gain hands-on experience, and explore future career opportunities in a fast-paced, innovation-driven environment. You will join Intel Foundry, an organization focused on advancing semiconductor manufacturing technologies and delivering solutions to customers worldwide. The team works to optimize manufacturing processes, sustain equipment performance, and drive production excellence in support of Intel's technological leadership.

Requirements

Must be actively pursuing a PhD in Materials Science Engineering, Physics, Chemistry, Electrochemistry, or Chemical Engineering.
At least 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.

Nice To Haves

Experience with data analysis, statistical methods, and process optimization techniques.
Cumulative GPA of 3.0 or higher.
Expertise in chemical reaction kinetics, selective wet etching, interfacial surface chemistry, corrosion/passivation mechanisms, or surfactant-assisted cleaning/drying mechanisms, such as Marangoni drying or supercritical CO2.
Hands-on experience with advanced thin-film, surface, and chemical characterization techniques, such as 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.
Experience using 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.

Responsibilities

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.