Senior R&D Device Test and Characterization Engineer

The Characterization and Test Engineer will play a key role in the Scottsdale Device Characterization Laboratory, supporting semiconductor technology development through wafer-level electrical characterization, reliability testing, and test automation. This position combines hands-on semiconductor testing with Python-based system control, large-scale data analysis, and the practical integration of artificial intelligence and machine learning into laboratory workflows.

The successful candidate will operate advanced semiconductor measurement systems and semi-automatic probe stations, develop automated test solutions, analyze complex data sets, and deliver accurate, timely, and traceable engineering results. The engineer will also help modernize laboratory operations by applying AI-assisted development, machine-learning methods, automated data-quality checks, anomaly detection, and intelligent report generation where these approaches provide measurable improvements in test efficiency and data quality.

This position is well suited for an engineer who enjoys working directly with semiconductor devices and laboratory equipment while also using software, data analytics, and emerging AI technologies to solve practical engineering problems. Candidates should be comfortable taking ownership of test systems, troubleshooting hardware and software, learning unfamiliar measurement techniques, and converting engineering requirements into reliable and scalable test workflows.

• Perform wafer-level electrical characterization and reliability testing using various SMUs (such as B1500) and Semi and Fully-automatic probe stations, including I-V, C-V, breakdown-voltage, leakage-current, HTRB, HCI, TDDB, RTS, and low-frequency-noise measurements.
• Develop new device characterization test routines and procedure for emerging technologies.
• Translate engineering requests into accurate test configurations, execute measurements, validate data quality, identify abnormal results, and deliver timely, traceable reports.
• Develop and maintain Python scripts for instrument control, wafer stepping, automated test execution, data processing, visualization, and analysis of large semiconductor data sets.
• Integrate AI, machine learning, statistical analysis, and anomaly-detection methods into testing workflows to improve data review, test efficiency, equipment utilization, and report generation.
• Own assigned test systems and support equipment scheduling, calibration, preventive maintenance, upgrades, troubleshooting, wafer traceability, laboratory compliance, and user training.
• Collaborate with device engineers, laboratory personnel, facilities teams, equipment vendors, and external partners to expand characterization capabilities and resolve test-system issues.

• Bachelor’s degree or higher in Electrical Engineering, Semiconductor Engineering, Physics, Materials Science, Computer Engineering, or a closely related field, or equivalent relevant industry experience.
• Hands-on experience with semiconductor wafer-level characterization, semi-automatic probe stations, and semiconductor parameter analyzers such as the Keysight B1500A or B1505A, Keithley K2612, 2657, or similar.
• Practical knowledge of semiconductor devices, electrical characterization, reliability testing, high-voltage measurements, low-current measurements, and temperature-dependent testing.
• Proficiency in Python, including independent development and debugging of scripts for instrument control, test automation, data processing, visualization, and analysis of large data sets.
• Familiarity with Python engineering libraries such as NumPy, pandas, SciPy, and Matplotlib, and with instrument-control protocols or test-automation platforms.
• Ability to troubleshoot integrated hardware and software systems and distinguish valid device behavior from measurement noise, setup errors, equipment limitations, or data-processing issues.
• Working knowledge of statistics and an interest in applying AI and machine learning to anomaly detection, pattern recognition, predictive maintenance, test optimization, and engineering reporting.
• Strong technical communication, organization, documentation, and collaboration skills. Candidates are not expected to know every listed platform or measurement technique, but must demonstrate strong semiconductor fundamentals, hands-on laboratory capability, practical Python proficiency, and the ability to learn new systems independently.