Summer 2027 - Device Characterization Engineering Intern

An internship at onsemi offers a dynamic opportunity to gain hands-on experience through real-world projects that drive innovation in the semiconductor industry. Interns are entrusted with meaningful responsibilities and exposed to cutting-edge technologies, fostering both technical skill development and professional growth.

onsemi views internships as a strategic pipeline for entry-level career positions, providing a collaborative and inclusive environment where interns work alongside diverse teams and contribute to high-impact initiatives. The company’s commitment to sustainability, including its goal of achieving net-zero emissions by 2040, enables interns to engage in projects that align with global environmental priorities.

Key benefits include:

• Competitive compensation and medical benefits
• Flexible work hours aligned with academic schedules
• Access to Employee Resource Groups (ERGs) which foster a sense of belonging and provide opportunities for mentorship and community engagement
• Networking events, professional development workshops, and project showcases that build career readiness and visibility across the organization

Internship roles span multiple disciplines including engineering, business operations, data analytics, and finance, and are designed to align with onsemi’s strategic goals of innovation and workforce development. Structured programs such as rotational field sales and finance analyst tracks offer additional career pathways for interns transitioning into full-time roles.

Responsibilities

The Device Characterization Engineering Intern will support semiconductor device characterization and yield improvement activities within a high-volume manufacturing environment. The intern will work alongside Device, Integration, Probe/Test, and Process Engineering teams to analyze electrical test data, identify device and process-related performance trends, and develop data-driven approaches to improve product yield and manufacturing performance.

The role provides hands-on exposure to semiconductor device behavior, electrical characterization, wafer-level test data, statistical analysis, and structured problem solving.

Responsibilities

• Analyze wafer-level electrical and probe test data to identify yield trends, outliers, and systematic performance signatures.
• Perform data mining and statistical analysis to correlate electrical characteristics with process, device, and manufacturing variables.
• Support characterization of semiconductor devices across operating conditions and manufacturing variation.
• Investigate electrical parameters and device performance distributions to identify opportunities for yield improvement.
• Develop analytical models, visualizations, or automated methods to improve detection and understanding of device and yield trends.
• Work with Device, Integration, Probe/Test, and Process Engineering teams to investigate manufacturing and product performance issues.
• Apply structured problem-solving methods to identify potential drivers of electrical performance variation.
• Summarize technical findings and communicate conclusions and recommendations to engineering teams.
• Complete an internship project with a defined technical deliverable demonstrating measurable improvement or new insight into device/yield performance.

Qualifications

Required

• Currently pursuing a Bachelor's or Master's degree in Electrical Engineering
• Junior or senior undergraduate standing preferred.
• Coursework or fundamental understanding of semiconductor devices and basic electrical characteristics.
• Strong analytical and problem-solving skills.
• Experience analyzing technical or quantitative data.
• Ability to communicate technical results clearly and work effectively across engineering disciplines.

Preferred

• Familiarity with MOSFETs, semiconductor device physics, or electrical characterization.
• Exposure to statistical analysis, semiconductor manufacturing, wafer probe/test, or yield engineering.
• Experience with Python, MATLAB, JMP, Excel, or similar data-analysis tools.
• Familiarity with data visualization, statistical modeling, or machine-learning techniques.