Control-friendly scheduling algorithms for multi-tool, multi-product manufacturing systems

textThe fabrication of semiconductor devices is a highly competitive and capital intensive industry. Due to the high costs of building wafer fabrication facilities (fabs), it is expected that products should be made efficiently with respect to both time and material, and that expensive unit operations (tools) should be utilized as much as possible. The process flow is characterized by frequent machine failures, drifting tool states, parallel processing, and reentrant flows. In addition, the competitive nature of the industry requires products to be made quickly and within tight tolerances. All of these factors conspire to make both the scheduling of product flow through the system and the control of product quality metrics extremely difficult. Up to now, much research has been done on the two problems separately, but until recently, interactions between the two systems, which can sometimes be detrimental to one another, have mostly been ignored. The research contained here seeks to tackle the scheduling problem by utilizing objectives based on control system parameters in order that the two systems might behave in a more beneficial manner. A non-threaded control system is used that models the multi-tool, multi-product process in a state space form, and estimates the states using a Kalman filter. Additionally, the process flow is modeled by a discrete event simulation. The two systems are then merged to give a representation of the overall system. Two control system matrices, the estimate error covariance matrix from the Kalman filter and a square form of the system observability matrix called the information matrix, are used to generate several control-based scheduling algorithms. These methods are then tested against more tradition approaches from the scheduling literature to determine their effectiveness on both the basis of how well they maintain the outputs near their targets and how well they minimize the cycle time of the products in the system. The two metrics are viewed simultaneously through use of Pareto plots and merits of the various scheduling methods are judged on the basis of Pareto optimality for several test cases.Chemical Engineerin

Run by Run Control for Semiconductor Manufacturing

A new type of Run-by-Run controller based on the DHOBE (Dasgupta-Huang Optimal Bounding Ellipsoid) algorithm is designed and simulated for semiconductor manufacturing process. One approach is to use the algorithm to implement online model identification which leads to a model-reference controller. The other approach utilizes the worst case idea, to implement the set-valued controller. Both kinds of controllers are applied to linear and quadratic models which are derived from experiments. The controllers are simulated for cases when processes satisfy slow drifting, abrupt shift, bad data and model errors. The controllers are tuned according to the requirements of the algorithm and process and the simulation data is analyzed according to the performance benchmark. All the simulation results are compared to either the Exponentiallly Weighted Moving Average (EWMA) or Optimal Adaptive Quality Controller (OAQC) control method

Doctor of Philosophy

dissertationIn order to ensure high production yield of semiconductor devices, it is desirable to characterize intermediate progress towards the final product by using metrology tools to acquire relevant measurements after each sequential processing step. The metrology data are commonly used in feedback and feed-forward loops of Run-to-Run (R2R) controllers to improve process capability and optimize recipes from lot-to-lot or batch-to-batch. In this dissertation, we focus on two related issues. First, we propose a novel non-threaded R2R controller that utilizes all available metrology measurements, even when the data were acquired during prior runs that differed in their contexts from the current fabrication thread. The developed controller is the first known implementation of a non-threaded R2R control strategy that was successfully deployed in the high-volume production semiconductor fab. Its introduction improved the process capability by 8% compared with the traditional threaded R2R control and significantly reduced out of control (OOC) events at one of the most critical steps in NAND memory manufacturing. The second contribution demonstrates the value of developing virtual metrology (VM) estimators using the insight gained from multiphysics models. Unlike the traditional statistical regression techniques, which lead to linear models that depend on a linear combination of the available measurements, we develop VM models, the structure of which and the functional interdependence between their input and output variables are determined from the insight provided by the multiphysics describing the operation of the processing step for which the VM system is being developed. We demonstrate this approach for three different processes, and describe the superior performance of the developed VM systems after their first-of-a-kind deployment in a high-volume semiconductor manufacturing environment

Modeling the oriented strandboard manufacturing process and the oriented strandboard continuous rotary drying system

Oriented Strand Board (OSB) is the leading structural panel product used in residential building construction. This dissertation describes three models and a statistical process control technique all designed to aid manufacturers to cost effectively manufacture OSB. The first model is an OSB Mill Process Flow Model that defines the processing steps and the desired outcomes. The second model is an OSB Mill Model, an ExcelRTM based computer program, designed to answer operational what if and trade-off questions. The model is a spreadsheet representation of the OSB production process. The third model is an OSB Dryer Model that predicts the dryer outlet moisture content derived using a multivariate data analysis technique called projection to latent structures by means of Partial Least Squares (PLS). PLS was instrumental in identifying outlet temperature and heat source temperatures as the most influential dryer system variables in predicting dryer outlet moisture content. The SPC technique is Multivariate Statistical Process Control (MSPC) that uses multivariate scores or Hotelling T2 to determine the state of the drying process; and if the drying process is out of control, what process variables influenced the process shift

Integrated model-based run-to-run uniformity control for epitaxial silicon deposition.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Also available online at the MIT Theses Online homepage Includes bibliographical references (p. 241-247).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Semiconductor fabrication facilities require an increasingly expensive and integrated set of processes. The bounds on efficiency and repeatability for each process step continue to tighten under the pressure of economic forces and product performance requirements. This thesis addresses these issues and describes the concept of an "Equipment Cell," which integrates sensors and data processing software around an individual piece of semiconductor equipment. Distributed object technology based on open standards is specified and utilized for software modules that analyze and improve semiconductor equipment processing capabilities. A testbed system for integrated, model-based, run-to-run control of epitaxial silicon (epi) film deposition is developed, incorporating a cluster tool with a single-wafer epi deposition chamber, an in-line epi film thickness measurement tool, and off-line thickness and resistivity measurement systems. Automated single-input-single-output, run-to-run control of epi thickness is first demonstrated. An advanced, multi-objective controller is then developed (using distributed object technology) to provide simultaneous epi thickness control on a run-to-run basis using the in-line sensor, as well as combined thickness and resistivity uniformity control on a lot-to-lot basis using off-line thickness and resistivity sensors.(cont.) Control strategies are introduced for performing combined run-to-run and lot-to-lot control, based on the availability of measurements. Also discussed are issues involved with using multiple site measurements of multiple film characteristics, as well as the use of time-based inputs and rate-based models. Such techniques are widely applicable for many semiconductor processing steps.by Aaron Elwood Gower-Hall.Ph.D

Contributions to statistical methods of process monitoring and adjustment

Ph.DDOCTOR OF PHILOSOPH