Yield Enhancement Engineer (RFAB)

Texas InstrumentsRichardson, TX
Onsite

About The Position

TI-RFAB is looking for an experienced Yield Enhancement Engineer to own end-to-end yield improvement across our analog and mixed-signal product portfolio. The engineer will drive defect reduction from detection to root-cause analysis through permanent corrective action and apply modern AI/ML techniques to accelerate defect classification and yield learnings. The engineer will be required to work closely with Process, Product, Integration, and Equipment Engineering teams in a high-paced, fully automated fab environment.

Requirements

  • 3 years relevant experience in Yield Enhancement within the semiconductor industry
  • Bachelor's degree in Materials Science Engineering, Electrical Engineering, Chemical Engineering, Chemistry, Physics, or a related field.
  • Strong silicon semiconductor process knowledge with hands on yield engineering experience in a high-volume fab.

Nice To Haves

  • Comfort working across functional modules – Process, Product, Integration and Equipment Engineering – and communicating findings clearly to technical and managerial teams.
  • Demonstrated strong analytical skills.
  • High sense of urgency, structured problem-solving, and a collaborative mindset.
  • Demonstrated ability to build strong, influential relationships.
  • Ability to work effectively in a fast-paced and rapidly changing environment.
  • Ability to take the initiative and drive for result.

Responsibilities

  • Lead data-driven defect-limited yield analysis across all product families, from defect discovery through root-cause investigation to corrective action.
  • Serve as a key technical responder for defect excursions with the ability to triage data and mobilize cross-functional teams quickly to resolution.
  • Apply wafer map analytics to fingerprint yield-limiting defects by toolset, chamber, reticle field etc.
  • Define and continuously improve inline inspection sampling strategies, balancing detection sensitivity, throughput, and cost.
  • Build predictive yield models by correlating inline defect density with electrical test outcomes and liaise with the process modules to implement virtual metrology to ultimately enable real-time risk scoring.
  • Partner with Quality Assurance to close the loop between customer-observed failures and in-fab defect detection.
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