Patent US6148268A successfully refreshed from Google Patents.
US6148268A
Expired / Lapsed
US
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Method for quality control and yield enhancement
Assignee: Individual
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Filing: 1998-06-02
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Granted: 2000-11-14
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Expires: 2018-06-02
Abstract
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.
Inventors
- 👤 Yongan Wu
- 👤 Constance J. Chang-Hasnain
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 18:24
CPC Classifications
G; G06; G06Q; G06Q10/00; G06Q10/04
Claims Analysis
Total Claims: 17
Claim 1
Independent Claim
96 words
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 κ nd a variance number ω; d) identifying potential error sources contributing to said error source number κ and said variance number ω; e) eliminating said error sources; and f) repeating steps (b) through (e) until said parameter is within a design requirement.
Claim 2
Independent Claim
96 words
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 κ nd a variance number ω; d) identifying potential error sources contributing to said error source number κ and said variance number ω; e) eliminating said error sources; and f) repeating steps (b) through (e) until said parameter is within a design requirement.
Claim 3
Dependent Claim
35 words
The method of claim 1 wherein, said product is a laser and said parameter is selected from the group consisting of operating voltage, threshold current, turn-on voltage, break-down voltage, contact resistance, differential resistance and capacitance.
Claim 4
Dependent Claim
49 words
The method of claim 1 wherein, said product is a semi-conductor laser and said parameter is selected from the group consisting of threshold current, threshold voltage, operating voltage, operating current, wall plug efficiency, differential efficiency, output power, characteristic temperature, quantum efficiency, internal loss, diode turn-on voltage and emission spectra.
Claim 5
Dependent Claim
45 words
The method of claim 1 wherein, said product is an integrated circuit and said parameter is selected from the group consisting of defect density, noise amplitude, signal to noise ratio, input power, input current, input voltage, output power, output voltage, output current and dark current.
Claim 6
Dependent Claim
35 words
The method of claim 1 wherein, said product is a transistor and said parameter is selected from the group consisting of gate turn-on voltage, gate leakage current, amplification coefficient, break-down voltage, gate capacitance and resistance.
Claim 7
Dependent Claim
29 words
The method of claim 1 wherein, said product is an optical detector and said parameter is selected from the group consisting of dark current, detectivity, responsivity, and wavelength spectra.
Claim 8
Dependent Claim
16 words
The method of claim 1 wherein, said chi-square function is replaced by an equivalent mathematical expression.
Claim 9
Independent Claim
95 words
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 κ and a variance number ω; d) identifying potential error sources contributing to said error source number κ and said variance number ω; c) eliminating said error sources; and f) repeating steps (b) through (e) until said manufacturing operation is within a design requirement.
Claim 10
Independent Claim
95 words
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 κ and a variance number ω; d) identifying potential error sources contributing to said error source number κ and said variance number ω; c) eliminating said error sources; and f) repeating steps (b) through (e) until said manufacturing operation is within a design requirement.
Claim 11
Dependent Claim
16 words
The method of claim 8 wherein, said chi-square function is replaced by an equivalent mathematical expression.
Claim 12
Independent Claim
142 words
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 κ and a variance number ω through said micro-processor; f) connecting said micro-processor to a display device; g) displaying said values for said error source number κ and said variance number ω on said display device; h) identifying error sources contributing to said error source number κ and said variance number ω; i) reducing said error sources; and j) repeating steps (b) through (i) until said parameter is within a design requirement.
Claim 13
Independent Claim
142 words
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 κ and a variance number ω through said micro-processor; f) connecting said micro-processor to a display device; g) displaying said values for said error source number κ and said variance number ω on said display device; h) identifying error sources contributing to said error source number κ and said variance number ω; i) reducing said error sources; and j) repeating steps (b) through (i) until said parameter is within a design requirement.
Claim 14
Independent Claim
140 words
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 κ and a variance number ω said micro-processor; f) connecting said micro-processor to a display device; g) displaying said values for said error source number κ and said variance number ω on said display device; h) identifying error sources contributing to said error source number κ and said variance number ω; i) reducing said error sources; and j) repeating steps (b) through (i) until said manufacturing operation is within a design requirement.
Claim 15
Independent Claim
140 words
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 κ and a variance number ω said micro-processor; f) connecting said micro-processor to a display device; g) displaying said values for said error source number κ and said variance number ω on said display device; h) identifying error sources contributing to said error source number κ and said variance number ω; i) reducing said error sources; and j) repeating steps (b) through (i) until said manufacturing operation is within a design requirement.
Claim 16
Independent Claim
126 words
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 κ and a variance number ω; d) identifying potential error sources contributing to said error source number κ and said variance number ω; e) changing said process parameter and calculating a dependence of said error source number κ and said variance number ω 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.
Claim 17
Independent Claim
126 words
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 κ and a variance number ω; d) identifying potential error sources contributing to said error source number κ and said variance number ω; e) changing said process parameter and calculating a dependence of said error source number κ and said variance number ω 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.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
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| 3.5 Years (Year 4) | 2003-11-14 | 2004-05-15 | 2004-11-14 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2007-11-14 | 2008-05-15 | 2008-11-14 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2011-11-14 | 2012-05-15 | 2012-11-14 | $7700.00 | Window Closed / Lapsed |