Traditional run-to-run controllers that rely on highly correlated historical events to forecast process corrections have been shown to provide substantial benefit over manual control in the case of a fab that is primarily manufacturing high volume, frequent running parts (i.e., DRAM, MPU, and similar operations). However, a limitation of the traditional controller emerges when it is applied to a fab whose work in process (WIP) is composed of primarily short-running, high part count products (typical of foundries and ASIC fabs). This limitation exists because there is a strong likelihood that each reticle has a unique set of process corrections different from other reticles at the same process layer. Further limitations exist when it is realized that each reticle is loaded and aligned differently on multiple exposure tools.A structural change in how the run-to-run controller manages the frequent reticle changes associated with the high part count environment has allowed for breakthrough performance to be achieved. This breakthrough was mad possible by the realization that; 1. Reticle sourced errors were highly stable over long periods of time, thus allowing them to be deconvolved from the day to day tool and process drifts. 2. Reticle sourced errors can be modeled as a feedforward disturbance rather than as discriminates in defining and dividing process streams. In this paper, we show how to deconvolve the static (reticle) and dynamic (day to day tool and process) components from the overall error vector to better forecast feedback for existing products as well as how to compute or learn these values for new product introductions - or new tool startups. Manufacturing data will presented to support this discussion with some real world success stories.
Driven by overlay shrinks and increasing product diversification in advanced fabs, automatic control of correctable overlay coefficients has become critical to semiconductor manufacturing. Although numerous reports have shown the compelling benefits of automatic run-to-run feedback control, one important issue has received very little attention to date. In many state-of-the-art fabs, reticle to wafer alignment is performed against marks that were printed at the first-or zero-level, whereas overlay is still measured between a target level and one or two reference levels. In many cases, perturbations of the reference level are unknown at the time of target level exposure. In this study, we will show how the perturbations of the reference level can impact overlay controllability at cascading levels (levels where overlay is measured against the reference level, but exposure tool alignment is done to the zero level). We will also show that once the perturbation is understood, it can be accounted for at the time of exposure, thus presenting an opportunity for additional overlay improvement.
SC778: Introduction to Advanced Process Control (APC) for Semiconductor Manufacturing
This course provides a comprehensive introduction to APC that will enable process control engineers to tackle the control issues they are facing in their manufacturing environments. Specifically, the participants will be introduced to the fundamentals of process control and fault detection and classification (FDC). The basic introduction will be supplemented with class exercises where participants will be involved in developing Run-to-Run control loops and FDC systems for semiconductor manufacturing processes such as deposition, polishing, litho and etch. Additionally, we will present a multi-scale approach to process control, where unit-level control is highlighted and differentiated from module-level (combination of 2 or more unit-level control loops) process control. The material is presented through a blend of theory, specific case studies, and the development of a check-list that will identify the type of APC system appropriate for various circumstances.