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Salary
≈ $143k – $263k per year (Estimated)
Location
In office (Gresham)
Seniority
Staff · 10+ years exp

Confirmed on the employer's own hiring board on Nov 6, 2026. First seen by Alion on Oct 30, 2026. onsemi scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Onsemi is an American semiconductor company that was spun out of Motorola in 1999 and has since narrowed itself from a broad commodity chip supplier into a focused power and sensing business. Its silicon carbide and silicon power devices go into electric vehicle traction inverters, chargers and industrial drives, while its image sensors are used in automotive cameras and industrial vision, and it has deliberately exited lower-margin product lines to concentrate on these markets. Headquartered in Scottsdale, Arizona and listed on Nasdaq, it owns its own silicon carbide substrate manufacturing to secure supply.

The Process Integration and Technology Development Engineer defines, develops, and implements silicon process technology for the manufacture of integrated silicon devices. The successful candidate will be responsible for unit/process module development, process flow implementation, design rule definition, device architecture, electrical characterization, and technology qualification. These tasks support new silicon technology developments and transfers of existing technologies in and out of the resident fab. Experience related to semiconductor processing in BCD, Discretes, and hybrid Smart Power technologies and knowledge of semiconductor device functions is required.

Process Integration and Technology Development

  • Lead the development of unit processes and process modules for CMOS, BCD, Discretes, and hybrid Smart Power technologies.
  • Define and assess process module characterization to ensure robustness, manufacturability, and scalability.
  • Develop and maintain end-to-end technology process flows with detailed process specifications.
  • Define physical design rules and test structures for characterization.
  • Drive process and device optimization to meet performance, reliability, and cost targets.
  • Design and analyze statistical experiments using DOE, regression, and AI-assisted methods to optimize device architecture and process modules.
  • Support intrinsic reliability studies, including gate oxide integrity, TDDB, HCI, EM, and stress migration, using both physics-based and data-driven approaches.

Manufacturing, Yield & Technology Transfer

  • Facilitate process improvements and corrective actions for yield, reliability, and manufacturability issues using root-cause analysis and data mining and analysis.
  • Support Process Engineering and Yield Engineering teams to improve process quality and yield for technologies released to manufacturing.

Enable smooth technology transfers into and out of the fab by using data-driven monitoring, excursion detection, and learning systems.

Required

  • MS or PhD in Electrical Engineering, Physics, Chemistry, Chemical Engineering, Materials Engineering, or related technical field.
  • Minimum of 10 years’ experience in semiconductor manufacturing or process development or process integration in a semiconductor manufacturing environment.
  • Knowledge of semiconductor processes such as oxidation, implant, deposition (CVD, PECVD, etc.), RIE, CMP, Lithography used in FEOL and BEOL wafer fabrication.
  • Experience applying DOE, statistical analysis, and data analytics to semiconductor development.
  • Understanding of semiconductor device physics and construction, including CMOS, DMOS, Bipolar, and Trench FET devices.
  • Strong written and verbal communication skills with the ability to convey complex technical concepts clearly.
  • Proven problem-solving ability and strong collaboration skills in cross-functional teams.

Preferred

  • Experience with 180 nm through 40 nm technologies.
  • Knowledge of intrinsic reliability testing methodologies, including GOI, VRamp, TDDB, HCI, EM, and SM.
  • Hands-on experience with process and device TCAD simulations.
  • Experience designing and evaluating physical and electrical design rule test structures.
  • Familiarity with AI/ML techniques, data mining, or statistical modeling applied to semiconductor process or device data.
  • Experience using scripting languages (e.g., Python, MATLAB, JMP, or equivalent) for data analysis, automation, or model development.
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