I graduated from the University of Virginia with a BS in Chemical Engineering in 2006.
I have spent the last 11 years down in Austin, TX working for Samsung Austin Semiconductor. I work in an engineering group called CVD - chemical vapor deposition. We manage the equipment and processes used for depositing thin film dielectrics onto silicon wafer substrates. Our most advanced products are made on 14nm process technology and are used within many major smartphones on the market. During this time I have experienced several factory start-ups, lead technology transfers, and learned manufacturing efficiency programs such as LEAN, Six Sigma, and TPM.
Why Data Science?
Semiconductor manufacturing involves highly complex chemical processes and advanced machinery that create the *billions of transistors needed to power modern CPUs. With transistor size on the order of nanometers, it's very difficult to see what you're working on! That being the case, as engineers we're highly dependent on the data produced by our manufacturing equipment (real-time hardware telemetry) and the limited metrology that we receive to fully understand the state of processes. With a manufacturing time to produce a complete chip at around 3 months, we generate terabytes of data during the life cycle of a single production wafer. This data provides the signals we need to ensure that our products are being made at a high quality.
I'm interested in melding my background in high-tech manufacturing with a strong foundation in Data Science so that I can make factories smarter - detect yield loss faster, predict when maintenance is needed, and improve production efficiency.
* I once read that transistors are produced in a greater quantity each year and at cheaper cost than grains of rice - wow!