Holland M Smith III, Ph.D.
Program Lead, Analytics and Equipment Integration
INFICON FPS
As Program Lead at INFICON FPS, Holland Smith leads Smart Manufacturing systems architecture and installation projects at 200mm and 300mm fabs across the world. Mr. Smith is a semiconductor data systems expert and a contributing developer of the INFICON FPS Data Warehouse, which enables advanced Fab Scheduling, optimized WIP movement, and other related capabilities of the INFICON FPS product suite. Mr. Smith owns the INFICON FPS Capacity Model product and is a published expert in automated calculations of key forecasting measures like throughput and cycle time that enable Smart manufacturing systems to model the future of a fab with high fidelity over different time scales.
Prior to INFICON FPS, Mr. Smith worked as a Technology Development Engineer at Intel D1D/X in Hillsboro, OR, specializing in Atomic Layer Deposition thin film process development as well as subfab improvement projects for harsh deposition processes. Holland lead several data integration projects including pioneering work to unite previously disparate subfab and fab-level data sources into an integrated and actionable system. Previous Mr. Smith also worked designing automated experimental data acquisition and analysis systems for semiconductor characterization at Lawrence Berkeley National Laboratory.
Mr. Smith earned a Ph.D. and M.S. in Materials Science and Engineering with a minor in Solid State Physics and a B.S.with Honors in Engineering Physics from University of California Berkeley, as well as a B.A. with Honors in Slavic Languages from Stanford University.
Alexa, Reduce My Cycle Time by 20%: A Data-Oriented Perspective on Progress Towards a Self-Driving Fab
As semiconductor factories continue to move toward the ideal of intelligent and automated decision-making, new pressures are being applied for the maintenance and integration of existing factory data systems and for methods of acquisition, storage and utilization of new data. Meanwhile, advances in both data architecture and computational technologies such as artificial intelligence and machine learning raise questions about the applicability of new tools to the world of semiconductor manufacturing. While I mean for the term “Self-Driving Fab” to be taken light-heartedly (for now), considerable effort is being invested in continuous improvement of automated decision-making in critical areas ranging from operations to yield, all relying on the fidelity and utility of a factory’s digital twin.
In this talk, I will aim to focus on the common types of factory data used for automated operational decision-making and control. I will present an order-of-magnitude overview of the scale of various types of data, as well as the present state of common architectures and protocols uniting them. The distinctions between logical, physical and simulated data will be discussed, as well as the different roles each type plays in running a fab and the potential for growth of each. I will give a historical perspective on some common design decisions of legacy data systems and identify areas where novel requirements may drive change. I will also offer a perspective on newer technologies such as cloud architecture and AI/ML in light of their suitability for enabling advances in automated fab operations, and what types of changes are required to support them.