SM Fab Advanced Process Controls (APC) Engineer

Texas Instruments; Sherman (TX); On-site; USD 95,000 - 150,000; Full time

Job summary

Texas Instruments is seeking a Factory Controls Engineer (APC) to join the team launching a new 300mm fab and driving continual improvement. You will work with Fab Engineering, process, equipment automation, data engineering and data science to develop APC solutions and predictive methods that reduce downtime and improve repeatability.

The role emphasizes APC development, MES familiarity, DOE/SPC, and integrating machine learning with traditional control to deliver world-class quality and

Responsibilities

• Support APC R2R processes to accelerate equipment/startup and SWRs.
• Support the ramp of a new factory with equipment installation, startup and release from a process perspective.
• Accelerate R2R control development: new features, APC tools, algorithms, and ML integration.
• Learn and operate multiple manufacturing execution systems (MES) quickly and proficiently.
• Support high-volume 300mm Fab operations in Analog or Embedded Processing Technologies.
• Develop and implement improved manufacturing procedures to boost efficiency, yields, and quality.
• Execute improvement plans for efficiency, cost reduction, or quality programs.
• Identify systematic problems in processes or equipment and communicate findings to engineering.
• Participate in data analysis and problem resolution.
• Develop new processes for existing or future technologies with high performance and low cost.
• Apply statistical rigor through SPC, DOE, modeling and other advanced data analysis methods.

Job description

Change the world. Love your job.

We can't predict what the future holds, but we know Texas Instruments will have a part in shaping it.

Texas Instruments is looking for an energetic engineer with a passion for process control and automation. This position provides the opportunity to start up a new factory and drive continual improvement at all levels. As a Factory Controls Engineer you are responsible for working with Fab Engineering to reduce run-to-run (R2R) process variation by implementing Advanced Process Control (APC) methods. In this position, you’ll have the opportunity to collaborate with process engineering, equipment automation, data engineering, and data science teams to develop innovative APC solutions and sophisticated methods of variation prediction to reduce unscheduled tool downtime, improve process repeatability/reliability, and protect the factory from wafer-loss events.

The responsibilities of a Factory Controls Engineer (APC) in this role include:

• Support the manufacturing ramp with execution of APC R2R processes that accelerate equipment/process startup and process experiments (SWRs)
• Support the manufacturing ramp of a new factory with execution of equipment installation from a process perspective, process startup, and process release
• Accelerate the development of the R2R control system, including new control features, creating APC software tools, optimizing algorithms, and integrating machine learning with traditional control
• Learn and execute in multiple manufacturing execution systems (MES) quickly and proficiently
• Support manufacturing operations in a high-volume manufacturing 300mm wafer Fab running Analog or Embedded Processing Technologies
• Develop and implement improved methods of manufacturing, procedures, and processes that result in improved efficiencies, yields, machine operations, and quality
• Implement project or improvement plans for areas of responsibility including but not limited to: efficiency improvements, cost reduction or quality improvement programs, new product support and other process improvements
• Identify systematic problems in either process or equipment related sources and communicating findings to the engineering staff in a continuous manner
• Participate in data analysis and problem resolution
• Develop new processes for existing or future technologies with high performance and low cost to produce world class quality
• Apply statistical rigor through SPC, DOE, modeling and other advanced data analysis methods