SBIR-STTR Award

This Small Business Innovation Research Phase I project will address two components required to automate the highly manual work of economic process control optimization in semiconductor manufacturing. The intelligent Real-time Economic Sampling (RES) system that can be created with the innovation will allow sampling to be optimized and adjusted many times per day across multiple process steps and products. This minimizes a manufacturer?s overall economic risk of producing bad products by adaptively focusing sampling where it gives the greatest financial return. The two research objectives are: 1) research modeling and optimization such that highly optimized sampling solutions for a whole semiconductor factory can be found in one hour (while using off-the-shelf affordable PC hardware), and 2) enable the RES system to estimate specialized yield parameters needed in real-time directly from aggregated inspection and yield data. Semiconductor manufacturers today are limited to occasional process control planning with time-consuming off-line analysis. Engineers also spend time doing manual ad-hoc adjustments to direct sampling where it is needed, while not really knowing what other harm they could be doing to the operation. Meanwhile valuable products are being wasted during out-of-control situations that can be detected faster if automatic economic sampling could collect data where the production risk is currently the highest. The RES system would step into a fast growing segment in the semiconductor industry, spending on process control went from 10% in 2000 to 19% in 2007 (Source: Dataquest), about $7.4 billion market. If successful, the RES tool could have a significant impact on the semiconductor process industry

This Small Business Innovation Research (SBIR) Phase II project will create a first Real-time Economic Sampling (RES) system for quality intensive high-tech manufacturing. Focusing initially on semiconductor manufacturing, the system will allow defect inspection sampling to be optimized and adjusted many times per day across all process tools, steps, and products. This minimizes, among other things, a manufacturer's economic risk of producing bad products by adaptively focusing sampling where it gives the greatest return. The complexity of the underlying interdisciplinary models have hindered the realization of RES. Recent advancements by the applicants showed feasibility that this research will build on. There are three main research categories, 1) Risk & Optimization, 2) Cycle-Time, and 3) Yield Modeling. Categories 1 & 2 build on Phase I feasibility results where fast, yet accurate, approximations of complex factory models were established. Risk & Optimization modeling will introduce near-term risk due to factory floor events into traditional (long-run average) risk models. Cycle-time research will refine the approximations for queues with heterogeneous servers. Yield modeling will introduce novel real-time yield approximations needed for RES. The included implementation tasks will then create the first factory-ready RES system that captures the necessary intricacies of semiconductor manufacturing. The proposed RES system is a "Green Manufacturing" enabler that creates a new market as it will be the first to recoup inefficiencies present in many process control operations. The initial focus is on semiconductor manufacturers who are limited by time-consuming off-line analysis, rigid sampling rules, and risky ad-hoc sampling adjustments. Meanwhile valuable products are being wasted due to out-of-control incidents and/or under-utilized inspection capacity. The proposed system can change this. An RES system belongs to a fast growing segment of the semiconductor industry: process control. Other markets can also be pursued and a societal impact due to reduced waste can become widespread