One of the main challenges to implementations of traditional run-to-run control in the semiconductor industry is a high mix of products in a single factory. To address this challenge, Just-in-time Adaptive Disturbance Estimation (JADE) has been developed. JADE uses a recursive weighted least-squares parameters estimation technique to identify the contributions to variation that are dependent on product, as well as the tools on which the lot was processed. As applied to photolithography overlay, JADE assigns these sources of variation to contributions from the context items: tool, product, reference tool, and reference reticle. Simulations demonstrate that JADE effectively identifies disturbances in contributing context items when the variations are known to be additive. The superior performance of JADE over traditional EWMA is also shown in these simulations. The results of application of JADE to data from a high mix production facility show that JADE still performs better than EWMA, even with the challenges of a real manufacturing environment.
KEYWORDS: Process control, Telecommunications, Metrology, Manufacturing, Semiconductors, Interfaces, Control systems, Data storage, MATLAB, Data modeling
This paper discusses the integration and development of advanced process control technologies with AMD's Fab25 factory systems using the Advance Process Control Framework. The Framework is an open software architecture that allows the integration of existing factory systems, such as the manufacturing execution systems, configurable equipment interfaces, recipe management systems, metrology tools, process tools, and add-on sensors, into a system which provides advanced process control specific functionality. The Advanced Process Control Framework project was formulated to enable effective integration of Advanced Process Control applications into a semiconductor facility to improve manufacturing productivity and product yields. The main communication link between the factory system and the Framework is the Configurable Equipment Interface. It interfaces through a specialized component in the framework, the Machine Interface, which converts the factory system communication protocol, ISIS, to the Framework protocol, CORBA. The Framework is a distributed architecture that uses CORBA as a communication protocol between specialized components. A generalized example of how the Framework is integrated into the semiconductor facility is provided, as well as a description of the overall architecture used for process control strategy development. The main development language, Tcl/Tk, provides for increased development and deployment over traditional coding methods.
With the introduction of high-throughput, multi-arm chemical-mechanical planarization (CMP) tools, a new source of process variation is introduced to the CMP process. These arm-to-arm variations are caused by small differences in the polishing rates of each arm. Modeling the arm-to-arm interactions of a CMP tool allows the application of a model-based, multi-variable, run-to-run control scheme. This control scheme is an optimization-based approach using pilot wafers applied to 'out of control' processes.In addition, this paper outlines a controller that can be applied directly to production wafers. The production-based run-to- run controller allows for monitoring the process through statistical process control methods and utilizes known relationships between product and pilot wafer removal rates in order to keep the process 'in control'. The product-based controller will be automated and deployed in AMD's Fab25- micron facility in Austin, TX using the software developed under the Advanced Control Framework Initiative.
KEYWORDS: Process modeling, Control systems, Semiconducting wafers, Chemical mechanical planarization, Data modeling, Device simulation, Polishing, Process control, Semiconductor manufacturing, Manufacturing
Many steps in the manufacturing of semiconductors offer no opportunity for real-time measurement of the wafer state, necessitating the use of pre- and post-process measurements of the wafer state in a run-to-run control algorithm. The predominant algorithm in the industry is an extended form of SPC using an EWMA filter to adjust a model parameter vector using the available measurements. This paper evaluates the merits of using an optimal discrete controller relying on a discrete-time constrained state-space process model that incorporates feedforward action using the pre-process measurement and feedback using the post-process measurement, accounts for the process statistics using a noise model and optimal filtering theory, and ensures integral action in the controller by estimating unmeasured disturbances. Comparison to the EWMA algorithm are presented using simulations based on actual plant data from a chemical-mechanical polishing application. The polish process is particularly suitable for the application of such a controller because of the natural method the controller provides for incorporating unmeasured disturbances, like pad and slurry changes, in the control action.
Conference Committee Involvement (3)
Data Analysis and Modeling for Process Control II
3 March 2005 | San Jose, California, United States
Data Analysis and Modeling for Process Control
26 February 2004 | Santa Clara, California, United States
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.