Method for quality control and yield enhancement
OwnerYongan Wu, Constance J. Chang-Hasnaininventors
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Abstract
An efficient method for optimization of product characteristics or manufacturing processes. The method utilized a Chi-square model to identify error sources that contribute to the product or process variability. The method is particularly useful for evaluation of processes related to optics and electronics fabrication but also has application in fields where a chi-square distribution is observed.
Claims12
A method of optimizing a process affecting a parameter of a product, said method comprising the following steps:
- a) selecting a predetermined number of said product;
- b) measuring said parameter for each said product to obtain a parameter distribution;
- c) fitting said parameter distribution to a Chi-square function having an error source number .kappa. nd a variance number .omega.;
- d) identifying potential error sources contributing to said error source number .kappa. and said variance number .omega.;
- e) eliminating said error sources;
- and
- f) repeating steps (b) through (e) until said parameter is within a design requirement.
A method of optimizing a manufacturing operation, said method comprising the following steps:
- a) selecting a predetermined number of said manufacturing operations;
- b) measuring a variable parameter for each said manufacturing operation to obtain a parameter distribution;
- c) fitting said parameter distribution to a Chi-square function having an error source number .kappa. and a variance number .omega.;
- d) identifying potential error sources contributing to said error source number .kappa. and said variance number .omega.;
- c) eliminating said error sources;
- and
- f) repeating steps (b) through (e) until said manufacturing operation is within a design requirement.
A method of optimizing a process affecting a parameter of a product, said method comprising the following steps:
- a) selecting a predetermined number of said product;
- b) measuring said parameter for each said product to obtain a parameter data set;
- c) inputting said parameter data set into a micro-processor;
- d) relating said parameter data set to a Chi-square function through said micro-processor;
- e) generating values for an error source number .kappa. and a variance number .omega. through said micro-processor;
- f) connecting said micro-processor to a display device;
- g) displaying said values for said error source number .kappa. and said variance number .omega. on said display device;
- h) identifying error sources contributing to said error source number .kappa. and said variance number .omega.;
- i) reducing said error sources;
- and
- j) repeating steps (b) through (i) until said parameter is within a design requirement.
A method of optimizing a manufacturing operation, said method comprising the following steps:
- a) selecting a predetermined number of said manufacturing operations;
- b) measuring a variable parameter for each said manufacturing operation to obtain a parameter data set;
- c) inputting said parameter data set into a micro-processor;
- d) relating said parameter data set to a Chi-square function through said micro-processor;
- e) generating values for an error source number .kappa. and a variance number .omega. said micro-processor;
- f) connecting said micro-processor to a display device;
- g) displaying said values for said error source number .kappa. and said variance number .omega. on said display device;
- h) identifying error sources contributing to said error source number .kappa. and said variance number .omega.;
- i) reducing said error sources;
- and
- j) repeating steps (b) through (i) until said manufacturing operation is within a design requirement.
A method of predicting a product characteristic resulting from a change in a process parameter comprising the following steps:
- a) selecting a predetermined number of said product;
- b) measuring said product characteristic for each said product to obtain a parameter distribution;
- c) fitting said parameter distribution to a Chi-square function having an error source number .kappa. and a variance number .omega.;
- d) identifying potential error sources contributing to said error source number .kappa. and said variance number .omega.;
- e) changing said process parameter and calculating a dependence of said error source number .kappa. and said variance number .omega. on said change;
- f) generating a Chi-square distribution curve for a proposed change for said process parameter;
- and
- g) analyzing said Chi-square distribution to predict said product characteristic.
Dependencies are inferred from the claim text.