Reliability and Inspection Intern

Scope of Position

The Summer 2027 intern will support the development and validation of laboratory methods that detect and characterize small defects in brittle materials and multi-material structures, either as damage forms during thermal-mechanical reliability stresses or through inspection afterwards. The work will establish what each method can and cannot detect, and how those detections relate to underlying failure mechanisms.

Under the guidance of technical staff, the intern will set up and calibrate sensing and inspection systems, run reliability experiments, analyze waveform and image data, and confirm detections against independent ground truth from microscopy, fractography,  sectioning, materials and property characterization and failure analysis. Depending on interests and program needs, the project may focus on acoustic emission or ultrasonic monitoring during thermal cycling, on commissioning and applying an automated imaging and machine vision inspection system, or on a combination of both. Results will also help calibrate physics-based models and empirical reliability predictions.

Program Benefits

• Competitive salaries
• Housing assistance
• Travel reimbursement

Intern Activities

• Social and networking events
• Volunteer opportunities
• Community Involvement

Key Responsibilities

• Review literature on non-destructive evaluation, acoustic emission and ultrasonic sensing, optical inspection, and reliability mechanisms in brittle materials and electronic packaging.
• Plan and run experiments, including building and calibrating sensors, data acquisition systems, and test or inspection setups.
• Apply acoustic emission or other ultrasonic methods to detect, locate and time damage during thermal cycling and other reliability-related stressors.
• Commission and apply a laboratory imaging system, including automated image analysis and machine vision for defect detection.
• Acquire, process and analyze sensor waveforms and image data using MATLAB, Python or similar data-analysis tools.
• Correlate detected events with independent ground truth from microscopy, sectioning, materials characterization and failure analysis; quantify detection thresholds, noise and false calls, and identify which damage mechanisms can and cannot be detected.
• Use collected data to help calibrate physics-based modeling predictions and develop empirical reliability predictions and associated models.
• Document methods so others can repeat them, follow laboratory safety practices, and communicate findings clearly in written reports and presentations.

Requirements

• Currently pursuing a graduate degree (PhD preferred) in mechanical, materials, reliability, electrical or computer engineering, applied physics, optics, or a closely related field.
• Research, coursework, or hands-on laboratory experience in at least one of: acoustic emission, ultrasonics or non-destructive evaluation; optical imaging or machine vision; reliability testing, life modeling or failure analysis.
• Experience with signal or image processing using MATLAB, Python or similar tools.
• Ability to follow laboratory safety procedures, experimental protocols, and data-documentation practices.
• Strong analytical and problem-solving skills, with attention to detail and a willingness to learn new hands-on laboratory techniques.
• Ability to organize, analyze, and communicate experimental results clearly in written and verbal formats.
• Ability to work collaboratively with scientists and engineers while independently completing assigned tasks.
• Must be available for 10 weeks during Summer 2027 (May-August 2027) with a graduation date of December 2027 or later
• Be interested in working for Corning, Inc after graduation, if presented with a full-time job opportunity.
• Be willing to work at the posted job location

Desired Skills

• PhD research in non-destructive evaluation, acoustics/ultrasonics, or imaging, applied to reliability of electronics and semiconductor packaging.
• Experience with acoustic emission sensors, waveguides, source location, waveform feature extraction, or laser vibrometry.
• Experience setting up cameras, optics, motion stages or automated inspection systems, including machine-learning-based image analysis.
• Experience with reliability testing of electronics or semiconductor packages, such as thermal cycling or environmental stress testing.
• Knowledge of brittle materials, glass, fracture mechanics, or thermal-mechanical modeling.
• Experience with microscopy, cross-sectioning, sample preparation, or other materials characterization methods.
• Familiarity with experimental design, statistical data analysis, and technical literature review.