SEMICONDUCTOR MANUFACTURING EQUIPMENT

• US 20130173042A1
• Filed: 09/13/2012
• Published: 07/04/2013
• Est. Priority Date: 12/28/2011
• Status: Active Grant

• Current Assignee: Hitachi High-Tech Company Limited (Hitachi, Ltd.)
• Original Assignee: Hitachi High-Technologies Corporation (Hitachi, Ltd.)
• Inventors: Morisawa, Toshihiro, Shiraishi, Daisuke, Inoue, Satomi, Kagoshima, Akira
• Granted Patent: US 9,110,461 B2
• Time in Patent Office: Days
• US Class Current: 700/121
• CPC Class Codes: G05B 19/4184 characterised by fault tolerance, reliability of production system
• CPC Class Codes: G05B 2219/32191 Real time statistical process monitoring
• CPC Class Codes: G05B 2219/45031 Manufacturing semiconductor wafers
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

Semiconductor manufacturing equipment includes: a controller controlling driving and processes of various parts of the semiconductor manufacturing equipment, and a sensor monitoring each physical amount in the semiconductor manufacturing equipment or a status of each chemical response amount; a database; and an arithmetic section executing: processing of reading out equipment data, calculating a correlation matrix between time points based on a plurality of pieces of signal data to be compared, calculating eigen values and eigen vectors from the correlation matrix, and calculating principal component scores by principal component analysis; processing of comparing magnitudes of the eigen values of the principal components, arranging the eigen values in descending order to display a list thereof; and processing of displaying a scatter diagram where the principal component scores of the respective signals are plotted in a feature space selecting the principal component corresponding to the eigen value having a contribution ratio.

Claims

1. Semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing, the semiconductor manufacturing equipment comprising:

a controller controlling driving and processes of various parts of the semiconductor manufacturing equipment, and a sensor monitoring each physical amount in the semiconductor manufacturing equipment or a status of each chemical response amount;

a database storing equipment data obtained by sampling, at predetermined intervals, output signals of the controller and the sensor of the semiconductor manufacturing equipment being manufactured; and

an arithmetic section executing;

processing of searching the database for the equipment data of the semiconductor manufacturing equipment to be evaluated, reading out the equipment data to be analyzed, calculating a correlation matrix between time points based on a plurality of pieces of signal data to be compared, calculating eigen values and eigen vectors from the correlation matrix, and calculating principal component scores by principal component analysis;

processing of comparing magnitudes of the eigen values of the principal components, arranging the eigen values in descending order to display a list thereof on a user interface screen, thereby enabling determination of the eigen value having a contribution ratio; and

processing of displaying on the user interface screen a scatter diagram where the principal component scores of the respective signals are plotted in a feature space where the principal component corresponding to the eigen value having the contribution ratio is selected.

2. The semiconductor manufacturing equipment according to claim 1,

wherein the arithmetic section further executes processing of displaying on the user interface screen a graph of the principal component vectors corresponding to the eigen values having the contribution ratios with processing time plotted at a horizontal axis in order of vector element numbers and with vector values plotted at a vertical axis.

3. The semiconductor manufacturing equipment according to claim 1,

wherein the arithmetic section further determines a fault and outputs an fault occurrence alarm in a case where, with a previously inputted fault detection determination criterion being defined as a threshold, difference in principal component score value between the signals exceeds the threshold.

4. The semiconductor manufacturing equipment according to claim 1,

wherein the arithmetic section, upon detecting signal change based on similarity between the signals by using transformation values as the principal component scores, obtains, in descending order of contribution ratios (eigen values) of the principal component analysis, the number n of principal components until reach of a specified accumulated contribution ratio, and evaluates the similarity by using values of the principal components from the first principal component to the n-th principal component score.

5. The semiconductor manufacturing equipment according to claim 1,

wherein the arithmetic section further sequentially selects the principal component i corresponding to the eigen value having the contribution ratio, calculates, based on the principal component scores x_ij (where j is an index meaning a signal) of all the signals with respect to the principal component i, an average x_i (“

-”

on a variable means the average) and standard deviation σ

_i, determines that the signal not satisfying a formula below is a faulty signal;

x_i−

kσ

_i≦

x_ij≦

x_i+kσ

_i  

 

[Formula 1]

where k is a positive number (real number), and outputs information specifying the signal determined to be faulty.

12. The semiconductor manufacturing equipment according to claim 1,

wherein the arithmetic section is divided into:

a first arithmetic subsection included in the semiconductor manufacturing equipment; and

a second arithmetic subsection included in equipment data monitoring equipment connected via a network,

the database is installed in a manner such as to be connected to the semiconductor manufacturing equipment via the network,

the first arithmetic subsection records, onto the database via the network and the second arithmetic subsection, the equipment data obtained by sampling the output signals of the controller and the sensor of the semiconductor manufacturing equipment,

the second arithmetic subsection searches the database for the equipment data of the semiconductor manufacturing equipment to be evaluated, reads out the equipment data to be analyzed, quantitatively transforms characteristic change difference between a plurality of pieces of diagonal data to be compared, executes processing of specifying, based on the similarity between the signals by using a value of the transformation, the signal that can be identified as being faulty, and transmits to the first arithmetic subsection output put into a graph of quantitative values between the plurality of signals and alarm output provided by fault detection, and

the first arithmetic subsection further outputs onto the user interface screen the graph information and the fault detection alarm information received from the second arithmetic subsection.

6. A semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing, the semiconductor manufacturing equipment comprising:

a controller controlling driving and processes of various parts of the semiconductor manufacturing equipment, and a sensor monitoring each physical amount in the semiconductor manufacturing equipment or a status of each chemical response amount;

a database storing equipment data obtained by sampling, at predetermined intervals, output signals of the controller and the sensor of the semiconductor manufacturing equipment being manufactured; and

an arithmetic section executing;

processing of searching the database for the equipment data of the semiconductor manufacturing equipment to be evaluated, reading out the equipment data to be analyzed, between any two of a plurality of pieces of signal data to be compared, defining as distance between the signals a sum of squares of signal intensity difference over different sampling time points, obtaining a distance matrix and an inner product matrix, calculating eigen values and eigen vectors of the inner product matrix, and calculating a coordinate value of each signal in an MDS map by multi-dimensional scaling;

processing of comparing magnitudes of the eigen values corresponding to the respective signals and arraying the magnitudes in descending order to display a list thereof on a user interface screen, thereby enabling determination of the eigen values having contribution ratios; and

processing of displaying on the user interface screen a scatter diagram plotting a coordinate value of each signal in a feature space where an MDS map coordinate axis corresponding to the eigen value having the contribution ratio is selected.

7. The semiconductor manufacturing equipment according to claim 6,

wherein the arithmetic section further determines a fault and outputs an fault occurrence alarm in a case where, with a previously inputted fault detection determination criterion being defined as a threshold, difference in MDS map coordinate value between the signals exceeds the threshold,.

8. The semiconductor manufacturing equipment according to claim 6,

wherein the arithmetic section, by using transformation values as the coordinate values, upon detecting signal change based on similarity between the signals, obtains the number n of dimensions in descending order of the contribution ratios (eigen values) of the multi-dimensional scaling until reach of a specified accumulated contribution ratio, and uses the coordinate values for a first axis (the first dimension) to an n-th axis (the n-th dimension) to evaluate the similarity.

9. The semiconductor manufacturing equipment according to claim 6,

wherein the arithmetic section further sequentially selects the coordinate axis i corresponding to the eigen value having the contribution ratio, calculates, based on MDS map values x_ij (where j is an index meaning a signal) of all the signals with respect to the corresponding MDS map coordinate axis i, an average x_i (“

-”

on a variable means the average) and standard deviation a, determines that the signal not satisfying a formula below is a faulty signal;

x_i−

kσ

_i≦

x_ij≦

x_i+kσ

_i  

 

[Formula 2]

where k is a positive number (real number), and outputs information specifying the signal determined to be faulty.

13. The semiconductor manufacturing equipment according to claim 6,

wherein the arithmetic section is divided into:

a first arithmetic subsection included in the semiconductor manufacturing equipment; and

a second arithmetic subsection included in equipment data monitoring equipment connected via a network,

the database is installed in a manner such as to be connected to the semiconductor manufacturing equipment via the network,

the first arithmetic subsection records, onto the database via the network and the second arithmetic subsection, the equipment data obtained by sampling the output signals of the controller and the sensor of the semiconductor manufacturing equipment,

the second arithmetic subsection searches the database for the equipment data of the semiconductor manufacturing equipment to be evaluated, reads out the equipment data to be analyzed, quantitatively transforms characteristic change difference between a plurality of pieces of diagonal data to be compared, executes processing of specifying, based on the similarity between the signals by using a value of the transformation, the signal that can be identified as being faulty, and transmits to the first arithmetic subsection output put into a graph of quantitative values between the plurality of signals and alarm output provided by fault detection, and

the first arithmetic subsection further outputs onto the user interface screen the graph information and the fault detection alarm information received from the second arithmetic subsection.

10. Semiconductor manufacturing equipment including configuration for realizing wafer process processing and preparatory processing, the semiconductor manufacturing equipment comprising:

a controller controlling driving and processes of various parts of the semiconductor manufacturing equipment, and a sensor monitoring each physical amount in the semiconductor manufacturing equipment or a status of each chemical response amount;

a database storing equipment data obtained by sampling, at predetermined intervals, output signals of the controller and the sensor of the semiconductor manufacturing equipment being manufactured; and

an arithmetic section executing;

processing of searching the database for the equipment data of the semiconductor manufacturing equipment to be evaluated, reading out the equipment data to be analyzed, from a plurality of pieces of signal data to be compared, taking and squaring difference of the same signal between sampling time points, defining a sum for all the signals as distance between the time points, calculating a distance matrix and an inner product matrix based on distance relationship between the time points, calculating eigen values and eigen vectors of the inner product matrix, and calculating MDS scores based on a coordinate value matrix and a distance matrix of a feature space according to multi-dimensional scaling;

processing of comparing magnitudes of the eigen values corresponding to the respective signals and arraying the magnitudes in descending order to display a list thereof on a user interface screen, thereby enabling determination of the eigen values having contribution ratios; and

processing of displaying on the user interface screen a waveform graph of MDS scores corresponding to the eigen values having the contribution ratios with processing time plotted in order of vector device numbers at a horizontal axis and with values of the MDS scores plotted at a vertical axis.

11. The semiconductor manufacturing equipment according to claim 10,

wherein the arithmetic section, upon detecting signal change based on similarity between the signals by using the MDS scores as characteristics of the signals for the respective axes of the feature space, obtains, in descending order of contribution ratios (eigen values) of inter-time-point multi-dimensional scaling, the number n of dimensions until reach of a specified cumulative contribution ratio, and uses the MDS scores of the signals for the first (first dimension) to n-th (n-th dimension) axes to evaluate the similarity.

14. The semiconductor manufacturing equipment according to claim 10,

wherein the arithmetic section is divided into:

a first arithmetic subsection included in the semiconductor manufacturing equipment; and

a second arithmetic subsection included in equipment data monitoring equipment connected via a network,

the database is installed in a manner such as to be connected to the semiconductor manufacturing equipment via the network,

the first arithmetic subsection records, onto the database via the network and the second arithmetic subsection, the equipment data obtained by sampling the output signals of the controller and the sensor of the semiconductor manufacturing equipment,

the second arithmetic subsection searches the database for the equipment data of the semiconductor manufacturing equipment to be evaluated, reads out the equipment data to be analyzed, quantitatively transforms characteristic change difference between a plurality of pieces of diagonal data to be compared, executes processing of specifying, based on the similarity between the signals by using a value of the transformation, the signal that can be identified as being faulty, and transmits to the first arithmetic subsection output put into a graph of quantitative values between the plurality of signals and alarm output provided by fault detection, and

the first arithmetic subsection further outputs onto the user interface screen the graph information and the fault detection alarm information received from the second arithmetic subsection.