SM Factory Controls Engineer (APC)

Texas Instruments•Sherman, TX

About The Position

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.

Requirements

3+ years relevant experience
Bachelor’s degree in Computer Science, Software Engineering, Electrical Engineering, Mechanical Engineering, Chemical Engineering, Materials Science, or a related field
Strong foundation in programming languages, Python, and/or SQL
Experience with statistical process control, control theory/tools (e.g., PID tuning, state estimation, etc.), linear algebra, engineering change control and process release strategies

Nice To Haves

Advanced technical degree related to controls, systems, or software development
Prior experience with Semiconductor APC systems, preferably AMAT E3 or ProcessWORKS
Sound understanding of relational databases, data structures, automation, scripting, software development best practices, collaborative CI/CD code repository tools, testing/validation, etc.
Understanding of semiconductor process flow/integration
Previous wafer fab and/or startup experience
Demonstrated analytical and problem-solving skills
Strong written, visual, and verbal communication skills
Ability to work in teams and collaborate effectively with people in different functions
Strong time management skills that enable on-time project delivery
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 results
Applied experience with statistics, model predictive control (MPC), data science, machine learning integration, artificial intelligence, or other related fields
Comfortable in a Unix or Linux environment with the command line
Experience with FDC software (e.g., TIMS, Artist, AMAT E3, Fabguard) is a plus

Responsibilities

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 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 communicate 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