Yield Enhancement Engineer (RFAB)

Texas Instruments•Richardson, TX

9d•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

Experienced Yield Enhancement Engineer
Own end-to-end yield improvement
Drive defect reduction from detection to root-cause analysis through permanent corrective action
Apply modern AI/ML techniques to accelerate defect classification and yield learnings
Work closely with Process, Product, Integration, and Equipment Engineering teams
High-paced, fully automated fab environment experience
Lead data-driven defect-limited yield analysis
Serve as a key technical responder for defect excursions
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
Build predictive yield models by correlating inline defect density with electrical test outcomes
Liaise with the process modules to implement virtual metrology
Partner with Quality Assurance to close the loop between customer-observed failures and in-fab defect detection.

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.