Method and device of measuring breakdown status of equipment

A breakdown measuring method and a breakdown measuring device are disclosed. The breakdown measuring method includes the following steps: capturing a plurality of process flows, each of the process flows includes at least one recipe step; analyzing a flow attribute corresponding to each of the process flows; capturing sensing data corresponding to the at least one recipe step; generating a local feature in a time interval corresponding to each of the process flows according to the corresponding flow attribute, the corresponding at least one recipe step and the corresponding sensing data; generating a trend distribution according to the local features of the process flows; determining whether to send an alarm information based on the trend distribution.

CROSS-REFERENCE TO RELATED APPLICATIONS

This non-provisional application claims priority under 35 U.S.C. § 119(a) on Patent Application No(s). 104138459 filed in Taiwan, R.O.C. on Nov. 20, 2015, the entire contents of which are hereby incorporated by reference.

TECHNICAL FIELD

The disclosure relates to a method and device of measuring a breakdown status of equipment.

BACKGROUND

A conventional method of assessing the breakdown states of semiconductor manufacturing machines or the breakdown states of semiconductor manufacturing machines caused by aging usually depends on either the intensity of electrical signals (e.g. currents or voltages) or parameters (e.g. the quantity of output light) related to these electrical signals. However, this method requires a specific setting for assessing a specific kind of product. Also, it is heavily dependent on the professional knowledge in a related field to interpret the meanings of the above electrical signals or parameters.

Another conventional assessment method depends on individual standard thresholds for a specific manufacturing process, and these individual standard thresholds are predefined according to the sensing results of the manufacturing process. However, such predefined standard thresholds cannot be applied to the breakdown assessments of other manufacturing processes. Also, this method is heavily dependent on the many years experience or experimental study of experts in a related field to interpret the assessment result to obtain a malfunction level or an aging level.

SUMMARY

According to one or more embodiments, the disclosure provides a measurement method for breakdown of equipment, which includes the following steps. Acquire process flows. Each of the process flows includes at least one recipe step. Analyze a procedure attribute corresponding to each of the process flows. Acquire sensing information corresponding to the at least one recipe step. Generate a portion feature corresponding to a time period according to the procedure attribute, the at least one recipe step and the sensing information. Establish a trend distribution according to the portion features in the process flows. Determine whether to output a warning message according to the trend distribution.

According to one or more embodiments, the disclosure provides a measurement device for breakdown of equipment. The measurement device includes a process flow capturing unit, an attribute analysis unit, a sensing unit, a feature capturing unit, a trend determination unit and a warning unit. The process flow capturing unit acquires process flows, and each of the process flows includes at least one recipe step. The attribute analysis unit is coupled to the process flow capturing unit and analyzes a procedure attribute corresponding to each of the process flows. The sensing unit captures sensing information corresponding to the at least one recipe step. The feature capturing unit is coupled to the process flow capturing unit, the attribute analysis unit and the sensing unit and generates a portion feature according to the procedure attribute, the at least one recipe step and the sensing information during a time period. The trend determination unit is coupled to the feature capturing unit and establishes a trend distribution according to the portion features of the process flows. The warning unit is coupled to the trend determination unit and determines whether to output a warning message according to the trend distribution.

DETAILED DESCRIPTION

FIG. 1is a block diagram of a measurement device100for the breakdown of equipment in an embodiment. The measurement device100is used to measure the breakdown level or aging level of a machine. The measurement device100includes a process flow capturing unit110, an attribute analysis unit120, a sensing unit130, a feature capturing unit140, a trend determination unit150, a warning unit160and an interface presenting unit170. The attribute analysis unit120is coupled to the process flow capturing unit110, the feature capturing unit140is coupled to the process flow capturing unit110, the attribute analysis unit120, the sensing unit130and the trend determination unit150, the warning unit160is coupled to the trend determination unit150, and the interface presenting unit170is coupled to the trend determination unit150and the warning unit160.

The process flow capturing unit110, the attribute analysis unit120, the sensing unit130, the feature capturing unit140, the trend determination unit150and the warning unit160are carried out by, for example, but not limited to, a variety of chips or microprocessors. The interface presenting unit170is, for example, but not limited to, a variety of displays.

The process flow capturing unit110acquires multiple process flows. For example, a semiconductor manufacturing process of a certain product (i.e. LEDs) includes 2767 process runs, and the process run includes one or more process flows. For the follow-up analysis, the process flow capturing unit110acquires the process flows having similar property from the 2767 process runs. This will be described in detail later.

FIG. 2is a schematic view of a process run of No. 99 in an embodiment.

In this embodiment, each process flow includes at least one recipe step. The recipe step is categorized into a rising recipe step, a falling recipe step or a smooth recipe step. For example, if the recipe step belongs to a heating step in the semiconductor manufacturing process of LEDs, the rising recipe step is a recipe step related to a temperature rising stage, the falling recipe step is a recipe step related to a cooling stage, and the smooth recipe step is a recipe step related to a thermal preservation stage. Other embodiments may be contemplated in which the recipe step is a formulation related to pressures, flow rates or other parameters. To clarify the disclosure, the following instances of recipe step belong to a heating step in the semiconductor manufacturing process of LEDs.

The recipe step includes multiple setpoints (e.g. heating temperatures at specific time points). Accordingly, the sensing unit130acquires sensing information related to the recipe step. The sensing information herein includes sensing values (e.g. temperatures sensed at specific time points).

According to the recipe step, instances of procedure attribute of the process flow are classified into a rising attribute, a falling attribute, a rising to steady state attribute and a falling to steady state attribute. The attribute analysis unit120analyzes each process flow to discern that the procedure attribute of each process flow is the rising attribute, the falling attribute, the rising to steady state attribute or the falling to steady state attribute. As shown inFIG. 2, the process run of No. 99 includes a process flow210with the rising attribute, a process flow220with the falling attribute, a process flow230with the rising to steady state attribute, and a process flow240with the falling to steady state attribute.

FIGS. 3A and 3Bare schematic views of a result of performing a k-means algorithm to all process flows based on a smooth recipe step in an embodiment.

In this embodiment, the acquired process flows are related to the same cluster. For example, the process flow capturing unit110acquires the process flows having as similar properties among all the process flows of the 2767 process runs by a clustering algorithm, e.g. a k-means algorithm. Because in the smooth recipe step a product is being manufactured (e.g. a LED is heated to 1200˜1400° C. in an epitaxy process), the process flow capturing unit110sorts the data of the smooth recipe steps among all the process flows for the follow-up breakdown assessment. This will be described in detail later.

For example, the process flow capturing unit110, according to the executive temperatures and executive times of all the smooth recipe steps, performs a k-means algorithm. If a k value is set to 3, three clusters, e.g. a cluster1, a cluster2and a cluster3as shown inFIGS. 3A and 3B, whose properties are similar, are produced. InFIG. 3A, a point having a label1represents the data belonging to the cluster1, a point having a label2represents the data belonging to the cluster2, and a point having a label3represents the data belonging to the cluster3. Other embodiments may be contemplated in which a different algorithm may be used as the clustering algorithm. Also, to clarify the disclosure, the following process flows are exemplified by the process flows that are acquired by the process flow capturing unit110and related to the cluster1.

The feature capturing unit140, according to the process flow, the procedure attribute of the process flow, at least one recipe step, and the sensing information, generates a portion feature during a time period. The portion feature indicates a certain malfunction feature. In other words, the portion feature is a basis to recognize a malfunction level.

In this embodiment, the portion feature is related to a deviation level between at least one recipe step and the related sensing information during the time period. Moreover, in an embodiment, the attribute analysis unit120further determines that the procedure attribute belongs to the rising attribute, the falling attribute, the rising to steady state attribute or the falling to steady state attribute, and the feature capturing unit140, according to this determination result, calculates a characteristic value of the related deviation level. Various instances of the recognized procedure attribute are described below.

FIGS. 4A˜4D are schematic diagrams of a variety if portion features.FIG. 4Aillustrates a portion feature of the process flow with a rising attribute in an embodiment,FIG. 4Billustrates a portion feature of the process flow with a falling attribute in an embodiment,FIG. 4Cillustrates two portion features of the process flow with a rising to steady state attribute in an embodiment, andFIG. 4Dillustrates two portion features of the process flow with a falling to steady state attribute in an embodiment. As shown inFIGS. 4A˜4D, the solid line indicated by the label L1represents setpoints, and the dashed line indicated by the label L2represents sensing values.

In a situation, when the attribute analysis unit120determines that the procedure attribute of the process flow is a rising attribute or a falling attribute, the feature capturing unit140calculates an average deviation of deviations between these setpoints and the related sensing values and sets the average deviation as a characteristic value of the related deviation level. For example, if the procedure attribute of the process flow is a rising attribute as shown inFIG. 4Aor is a falling attribute as shown inFIG. 4B, a relatively large average deviation of deviations between the setpoints and the related sensing values indicates a relatively high malfunction level. In other words, in a temperature rising stage or a cooling stage, when it is more difficultly for a sensing value to approach the related setpoint, the breakdown is more serious. Therefore, the average deviations (i.e. the characteristic values) inFIGS. 4A and 4Bcorrespond to different portion features410and420of malfunction, respectively.

In another situation, when the attribute analysis unit120determines that the procedure attribute of the process flow is a rising to steady state attribute or a falling to steady state attribute, the feature capturing unit140calculates a maximum deviation of deviations between the setpoints and the related sensing values and sets this maximum deviation as an characteristic value of the related deviation level. For example, if the procedure attribute of the process flow is a rising to steady state attribute as shown inFIG. 4Cor is a falling to steady state attribute as shown inFIG. 4D, a relatively large maximum deviation of deviations between the setpoints and the related sensing values indicates a relatively high malfunction level. That is, in the duration of heating or cooling down to the thermal preservation stage, when the sensing value is still changing even after approaching the related setpoint, the breakdown is more serious. Accordingly, the maximum deviations (i.e. the characteristic values) inFIGS. 4C and 4Dcorrespond to different portion features430and440of malfunction, respectively.

In the same situation, other types of characteristic values may be used. When the attribute analysis unit120determines that the procedure attribute of the process flow is a rising to steady state attribute or a falling to steady state attribute, the feature capturing unit140calculates a deviation time corresponding to the deviations larger than a threshold among all deviations between the setpoints and the related sensing values, and sets this deviation time as a characteristic value of the related deviation level. For example, as shown inFIGS. 4C and 4D, a relatively large deviation time, corresponding to the deviations larger than the threshold among all deviations between the setpoints and the related sensing values, indicates a relatively high malfunction level. In other words, in the duration of heating or cooling down to the thermal preservation stage, when a time period used by the sensing value to achieve a related setpoint is relatively long, the breakdown is more serious. Therefore, the deviation times (i.e. the characteristic values), corresponding to the deviations larger than the threshold inFIGS. 4C and 4D, correspond to different portion features450and460of malfunction, respectively.

Note that since the executive times of the process flows belonging to the cluster1are relatively short (e.g. the executive times are in a range from 180 seconds to 2091 seconds) and the executive temperatures are relatively high (e.g. the executive temperatures are in a range from 742.3° C. to 1613° C.), it is quite suitable to use the cluster1to calculate the portion features corresponding to the process flows with the rising to steady state attribute.

As described above, the epitaxy process generally requires that LEDs are heated to a temperature ranging from 1200° C. to 1400° C. Therefore, in an embodiment, when the cluster1is predeterminedly used to calculate portion features of process flows with the rising to steady state attribute, the process flow, where the temperature is lower than 1200° C., will be eliminated in order to enhance the accuracy of breakdown assessment. That is, the accumulated energy in the process flow, where the temperature is higher than or substantially equal to 1200° C. and which has the rising to steady state attribute and belongs to the cluster1, is sufficient, so such abnormal situations (e.g. the sensing value of the malfunctioned machine changes unceasingly or requires a longer time to approach the related setpoint) may obviously be exposed.

FIG. 5Ais a schematic diagram of characteristic values of the process flow that has a rising to steady state attribute and is related to a cluster1, in a sliding window method in an embodiment,FIG. 5Bis a schematic diagram of first trend values obtained in relation to the sliding window method inFIG. 5Ain an embodiment, andFIG. 5Cis a schematic diagram of a trend distribution established according to the first trend values inFIG. 5Bin an embodiment.

The trend determination unit150establishes a trend distribution and a related warning threshold according to multiple portion features of the process flows. Specifically, in this embodiment, the trend determination unit150performs a smoothing process to each of the characteristic values to obtain a respective first trend value, and obtains the trend distribution and the related warning threshold according to the first trend values.

For example, as shown inFIG. 5A, No. 0109, No. 0110, No. 0118, No. 0120, No. 0124, No. 0126, No. 0128, No. 0130, No. 0133, No. 0135, No. 0137, No. 0139, No. 0141 and other serial numbers not shown in the drawing are serial numbers of the process flows belonging to the cluster1. The smoothing process herein is using the sliding window method to obtain the first trend value. InFIG. 5B, a related average is set as the first trend value.

In the sliding window method as shown inFIG. 5A, the trend determination unit150sets a window length N to be 10 and a shift length n to be 3, and then a first trend value of the average type is obtained as shown inFIG. 5B. The average value, which is acquired when the sliding window is at a position510, corresponds to the process flow of No. 0135, and the average value, which is acquired when the sliding window is at a position520, corresponds to the process flow of No. 0141. The detailed description of the correlation between the rest of the process flows (such as No. 0146, 0154, 0160 and so on) and the movement of the sliding window to other positions according to the shift length may be referred to the previous description and thus, will not be repeated hereinafter.

Next, the trend determination unit150depicts a trend distribution according to the first trend values, as shown inFIG. 5C, and displays this trend distribution by the interface presenting unit170. The warning unit160, according to the trend distribution, determines whether to output a warning message. In this or some embodiments, the interface presenting unit170displays a known malfunctioning component replacing time point in addition to displaying the trend distribution.

FIG. 6Ais a schematic diagram of first trend values of various types generated using the characteristic values inFIG. 5Ain another embodiment,FIG. 6Bis a schematic diagram of second trend values, corresponding to principal components1˜7and generated using the first trend values inFIG. 6A, in another embodiment,FIG. 6Cis a schematic diagram of a Fisher score of a maximum value calculated according to a different number of principal components in another embodiment,FIG. 6Dis a schematic diagram of a Mahalanobis distance array D generated according to the Fisher score of the maximum values in another embodiment,FIG. 6Eis a schematic diagram of a trend distribution established according to the Mahalanobis distance array D in another embodiment, andFIG. 6Fis a schematic diagram of a warning threshold generated using the trend distribution in another embodiment.

In this embodiment, the trend determination unit150establishes a trend distribution and a related warning threshold according to the portion features of the process flows. Specifically, in this embodiment, the trend determination unit150performs the smoothing process to each of the characteristic values to obtain an individual first trend value of a different type. For example, as shown inFIG. 6A, the trend determination unit150performs the sliding window method according to the characteristic values inFIG. 5Ato calculate the first trend values belonging to different types, such as minimum (Min), maximum (Max), mean, skewness, kurtosis, standard deviation (Std) and number of above mean (NAM), respectively.

The trend determination unit150performs a space transformation to these first trend values of different types to obtain related second trend values of different types. For example, the trend determination unit150performs a principal component analysis (PCA) to the first trend values of multiple types inFIG. 6Ato map these first trend values onto a principal component space, and thus, the second trend values respectively corresponding to the principal components1˜7inFIG. 6Bare obtained.

The trend determination unit150performs a dimensionality reduction to the second trend values corresponding to the principal components to obtain third trend values. For example, the trend determination unit150calculates a Fisher score for each principal component and, according to the maximum Fisher score, determines the amount of principal components to use. As shown inFIG. 6C, the trend determination unit150calculates the eigenvalues, proportions and cumulative proportions of the principal components1˜7. The proportion herein is obtained by dividing a sum of all eigenvalues by a related eigenvalue. For example, the proportion of the principal component1is 0.34208 (i.e. 1.9321/(1.9321+1.1783+1.0652+0.6763+0.4888+0.1655+0.1419)=0.34208). The cumulative proportions herein are obtained by accumulating the proportions from left to right. As shown inFIG. 6C, the eigenvalue at the left side is relatively large as compared to the eigenvalue at the right side, and this indicates that the principal component corresponding to the eigenvalue at the left side has a relatively high ability of interpreting the variability of original data (e.g. the data inFIG. 6A) as compared to the principal component corresponding to the eigenvalue at the right side.

Then, the trend determination unit150will learn that the principal component4has the maximum Fisher score and thus, has a rank of 1. Specifically, the Fisher score is a distribution distance determination index for two categories of samples. When the Fisher score is relatively large, the current two categories of data have a relatively large difference in distribution distance therebetween. Therefore, the trend determination unit150can, according to the Fisher score, determine the amount of principal components to use. Specifically, the calculation of a Fisher score needs to consider both the within-class scatter and the between-class scatter of two categories. A maximum Fisher score indicates a maximum proportion between the between-class scatter and the entire within-class scatter. In other words, when the within-class scatter (i.e. an individual group) is relatively small and the between-class scatter is relatively large (i.e. the difference between two categories), the trend determination unit150will obtain the amount of principal components to use, which is 4.

In this embodiment, the above two categories of data are two types of trend values before and after a time point of component replacement, respectively.

Accordingly, the trend determination unit150may calculate a Mahalanobis distance array (Mahalanobis distance matrix) D corresponding to the front4principal components, since the Mahalanobis distance array D is the most representative tendency data among the process flows. In other words, when a machine malfunctions or has aged, the tendency data generated by the front4principal components will express the machine's variation the most. A Mahalanobis distance array D includes multiple Mahalanobis distances d (i.e. the third trend values).

In details, the trend determination unit150calculates data Y according to the mapping data inFIG. 6B. The data Y is projection data constituted by the front N pieces of principal component having relatively large variance among the principal components1˜7. The trend determination unit150calculates the Mahalanobis distance d of each piece of data y in the data Y by the following equation:
d=√{square root over ((y−y)t(S)−1(y−y))},
where S represents a covariance matrix of all projections in the data y, andyis 0. Finally, the Mahalanobis distance array D corresponding to the front4principal components may be obtained, as shown inFIG. 6D. Each element in the Mahalanobis distance array D is a third trend value. The trend determination unit150uses these third trend values to depict the trend distribution as shown inFIG. 6E.

The warning unit160generates a warning threshold according to the trend distribution and compares the trend distribution with the warning threshold to determine whether to output a warning message. For example, the warning unit160uses a Cantelli inequality

Pr⁡(d>μD+a)≤σD2σD2+a2=λ
to generate a warning threshold T, as shown inFIG. 6F. μDrepresents a standard deviation of all values in the array D, λ is set as 0.05, and a represents a tolerance value higher than μD. When the warning unit160determines that in the trend distribution a data point exceeds T=μD+a, a warning message will be outputted. The production method of the warning threshold herein may also be used in the trend determination unit150in an embodiment, and other embodiments may be contemplated in which the trend determination unit150generates the warning threshold by other methods.

The interface presenting unit170is coupled to the trend determination unit150and the warning unit160and displays the above clusters, trend distribution, warning threshold, characteristic values, first trend values, second trend values, third trend values and/or the information about a warning message.

For example, the interface presenting unit170shows the above data in an interface by which a user may check them. Instances of the above data include recipe steps, the parameter (e.g. temperature, pressure, flow rate or other parameters) used in the recipe step, process flows, the procedure attribute of the process flow, the clustering diagrams of clusters, Fisher scores, principal components, tendency diagrams, a warning threshold, the time point of outputting a warning message, or other relevant data, such as replacing time points of known malfunctioned components.

Moreover, the user can, through what the interface shows, learn how many possible trend distributions may be used to assess the breakdown state. For example, assume that only one recipe step parameter (e.g. temperature) is in use, and the clustering algorithm in use supports 3 clusters (as shown inFIGS. 3A˜3B), characteristic values of 6 kinds of portion features (as shown inFIGS. 4A˜4D), 7 types of first trend values (e.g. the minimum value, the maximum value, the average value, the skewness, the kurtosis, the standard deviation and the number of averages). Then, there are 126 (i.e. 1*3*6*7=126) possible trend distributions to present the breakdown state of a machine.

In this embodiment, through the breakdown assessment and warning of the measurement device100as shown inFIG. 6F, the outputting of a warning message at is possibly advanced by 36 process runs in the breakdown assessment result as compared to a known component replacing time point t1(i.e. the process run of No. 1628). Alternatively, the outputting of a warning message is possibly advanced by 44 process runs in the breakdown assessment result as compared to a known component replacing time point t2(i.e. the process run of No. 2734), as shown by the period I2inFIG. 6F. Therefore, the measurement device100may efficiently avoid the cost of lose, which is caused by replacing a component after the component has malfunctioned.

FIG. 7is a flow chart of a measurement method for the breakdown of equipment in an embodiment. The measurement method for the breakdown of equipment includes steps S710˜S750. In step S710, the process flow capturing unit110acquires process flows, and each of the process flows includes at least one recipe step. In step S720, the attribute analysis unit120analyzes a procedure attribute of each of the process flows. In step S730, the sensing unit130acquires sensing information corresponding to the at least one recipe step. In step S740, the feature capturing unit140generates portion features according to the procedure attributes, at least one recipe step and the sensing information during a time period. In step S750, the trend determination unit150establishes a trend distribution according to the portion features of the process flows. In step S760, the warning unit160, according to the trend distribution, determines whether to output a warning message. These steps have been described above in detail and thus, they will not be repeated hereinafter.

As described above, the disclosure determines that the procedure attribute of each of the process flows belonging to the same cluster is a rising attribute, a falling attribute, a rising to steady state attribute or a falling to steady state attribute, so as to calculate a characteristic value of a portion feature indicating the deviation level of each process flow. In an embodiment, a smoothing process is performed to these characteristic values to obtain first trend values which are used to establish a trend distribution. In another embodiment, a smoothing process is performed to these characteristic values to obtain multiple types of first trend values, and a space transformation and a dimensionality reduction are performed to the first trend values to obtain third trend values, which are very suitable to represent malfunction levels and may be used to establish a trend distribution. Moreover, in an embodiment, a warning threshold is generated according to the trend distribution, and the comparison between the trend distribution and the warning threshold triggers whether a warning message is outputted or not. During the assessment of breakdown status, the breakdown assessment may automatically be performed by recognizing the portion features at a specific time point or in a specific time period according to according to the attribute of each process flow after recipe steps and the sensing data are acquired. Therefore, the disclosure may be applied to the manufacturing process of a variety of products and is able to be compatible to manufacturing formulations where a variety of different parameters (e.g. temperature, pressure and/or flow rate) is used. It may be possible to provide a common assessment standard and a friendly interface according to a variety of products or manufacturing processes.