About this Intern: AI for Silicon Test Data Analytics & Failure Analysis role at Cadence Design Systems
At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
Cadence is a pivotal leader in electronic design, building upon more than 30 years of computational software expertise. The company applies its underlying Intelligent System Design strategy to deliver software, hardware and IP that turn design concepts into reality.
Cadence customers are the world’s most innovative companies, delivering extraordinary electronic products from chips to boards to systems for the most dynamic market applications including consumer, hyperscale computing, 5G communications, automotive, aerospace industrial and health.
Location: Cadence Design Systems, Sophia Antipolis, France
Duration: 6 months
Profile: Final-year engineering student
About the Internship
Modern semiconductor testing generates large amounts of data. Turning this data into meaningful engineering insights quickly is becoming increasingly important.
During this internship, you will join the ATE Test team and explore how Artificial Intelligence, machine learning and advanced data analytics can help engineers understand silicon behaviour, identify failure patterns and extract useful information from semiconductor test results.
You do not need to be an AI or semiconductor test expert to apply. We are looking for someone who enjoys working with data, solving technical problems and finding patterns that can help engineers make better and faster decisions.
You will work alongside experienced test engineers and apply software and data-analysis techniques to real silicon test results.
What You Will Do
- Analyse semiconductor test results and engineering datasets.
- Explore AI and machine-learning techniques for automatically identifying patterns, anomalies and correlations in test data.
- Investigate clustering and classification approaches for silicon failures.
- Develop Python tools to automate test data processing and analysis.
- Explore automated analysis of characterisation data such as voltage, frequency and operating-condition sweeps.
- Investigate ways of correlating results across test conditions and silicon samples.
- Develop visualisations and summaries that make complex engineering results easier to understand.
- Explore how AI can assist engineers in moving from raw test data towards potential root causes and actionable insights.
- Collaborate with test engineers to evaluate solutions on real use cases.
Expected Deliverables
By the end of the internship, you will:
- Develop an automated data-analysis workflow for semiconductor test results.
- Demonstrate at least one AI or machine-learning technique applied to real test data.
- Develop meaningful visualisations or automated engineering summaries.
- Evaluate the ability of the solution to identify relevant patterns or anomalies.
- Document the approach, results and recommendations.
- Present your work to the engineering team.
Success Criteria
Success will be measured by the solution's ability to provide value in one or more of the following areas:
- Faster processing and interpretation of test results.
- Automatic identification of patterns, outliers or failure signatures.
- Improved correlation between different test results or operating conditions.
- Reduced manual effort in engineering data analysis.
- Improved visualisation and accessibility of silicon test information.
- Faster identification of areas requiring engineering investigation.
Preferred Qualifications
- Engineering or scientific background in electronics, microelectronics, computer science, data science or a related field.
- Basic Python programming skills.
- Interest in Artificial Intelligence, machine learning and data analytics.
- Comfortable working with numerical data.
- Analytical mindset and strong problem-solving skills.
- Curiosity and willingness to learn about semiconductor testing.
Nice to Have
- Experience with Python data-analysis libraries.
- Exposure to machine learning or statistical analysis.
- Knowledge of semiconductor devices, digital electronics or memories.
- Experience with data visualisation.
- Interest in silicon characterisation, validation or production testing.
What Matters Most
We are looking for curiosity and the ability to connect data with engineering problems. You do not need to arrive with deep knowledge of semiconductor testing. You will learn the test context while applying your software, data-analysis and AI skills to real engineering challenges.
This internship provides an opportunity to work at the intersection of semiconductor testing, data analytics and Artificial Intelligence, helping explore new ways of turning large volumes of silicon test data into useful engineering insights.
