Nevertheless, it is still necessary to reduce manufacturing costs and increase return on investment. Among other things, the semiconductor industry must decrease the use of nonproduct wafers, increase overall equipment effectiveness, reduce defects, improve product quality and yields, meet specifications for reduced feature sizes, improve production efficiency, and achieve faster process development and yield learning.

Achieving these and other objectives requires the implementation of process and equipment modeling, monitoring, and control, which are used widely in many industries and are beginning to take hold in the semiconductor industry, where such methods are commonly called advanced process control (APC) and advanced equipment control.

As device features shrink to 0.15 µm and below and 300-mm fabs proliferate, APC systems are becoming increasingly important. From the outset of 300-mm manufacturing, productivity improvements and fab effectiveness have been key considerations. To ensure the profitability of these new fabs, productivity gains must reach 30–35%.

Diagnosis, monitoring, and APC are needed to predict when tools require maintenance and to avoid unscheduled downtime. These control strategies also should reduce the mean time to repair. APC enables tools to perform with greater consistency, repeatability, and precision, leading fabs to increase production efficiency and reduce their total cost of tool ownership.

Even 200-mm fabs are discovering that the implementation of such control strategies is necessary to extend the lifetime of their process tools. In 200-mm fabs, implementing APC allows manufacturers to integrate new components such as sensors so that they can ensure that their tools keep pace with new technologies.

Figure 1: A two-dimensional T2 control chart showing error signatures for two detected errors with two variables, one of which is a function of the other.

Given the complexity of semiconductor processes and the need to adapt rapidly to technological changes, it is critical to choose optimal process and equipment modeling, monitoring, and control methodologies. This article, based on joint work between Si Automation (Montpellier, France) and STMicroelectronics (Crolles, France), explores the pros and cons of various process control strategies, focusing on global process control (GPC), a system for performing fault identification based on statistical and probabilistic criteria and for achieving automatic fault correction. GPC can be applied to process tool control through the use of data collector tools.

Comparing Process Control Methods

There are two major types of APC systems: statistical process control (SPC) and model-based process control (MBPC). SPC can be broken down into univariate and multivariate fault detection methods.

Fault Detection through Univariate SPC. Univariate SPC systems are based on the idea that variations of a controlled variable (variations that affect a process all the time and are essentially unavoidable within that process) have a common cause. SPC allows the normal (common-cause) variation of a controlled variable within acceptable limits and detects any assignable cause of abnormal variation of the controlled variable as soon as possible. The technique is characterized by the creation of control charts with a target value and upper and lower control limits that plot individual monitored variables.

Because univariate SPC leads to the creation of as many control charts as monitored variables, it is of no value at the fab level, where a huge number of variables must be monitored, and of little value at the tool level because an engineer can monitor a maximum of only four charts at the same time. Moreover, of all control methodologies, univariate SPC results in the most undetected errors and false alarms, leading to wasted time. Also, the monitored variables must theoretically be independent, which is usually not the case in the semiconductor industry.

Fault Detection through Multivariate SPC. Multivariate SPC is superior to univariate SPC. Rather than create separate control charts for each variable, multivariate SPC uses global control charts that represent as many variables as needed. Moreover, multivariate SPC is much more precise than univariate SPC in detecting errors and avoiding false alarms. Finally, multivariate SPC takes into account the dependencies among monitored variables.

Multivariate methodologies have their limitations, however. While they generate almost no false alarms or undetected errors, they cannot indicate the origin of errors and are thus unable to automatically manage error occurrences. Detecting error origins with these methodologies can be so complex that engineers either require a very high level of expertise and intuition to use them or cannot use them at all.

Model-Based Process Control. MBPC directly links product variables and process variables to models. This control method tries to determine the variables of a process from the characteristics of a desired output. Inversely, it also tries to predict product output from the process variables. These two applications are the basis for feed-forward, feed-backward, and real-time control. The power of the models is their ability to compare desired and real output variables.

Building models for MBPC requires a good knowledge of equipment and processes. While performing MBPC on one type of equipment may provide good results, doing so on another may provide unsatisfactory results. In all cases, performing MBPC requires that customers learn the method or that suppliers learn the processes, resulting in slow, complicated, and relatively inflexible implementation. But the main limitation of MBPC is theoretical: it acts on measured variables to obtain a desired output but gives no indication about the underlying causes of observed deviations. Finally, MBPC corrects the symptoms of errors but not the errors themselves, resulting in fewer out-of-control outputs but more-significant problems.

Global Process Control

An optimal control methodology must be able to perform automatic fault identification and single out anomalies when detected errors do not fit existing classifications. Few available tools indicate error types, let alone errors themselves, and those that do require that engineers analyze monitored profiles when faults are detected. That procedure can lead to misjudgments because profiles are not always obvious. Moreover, engineer intervention is time-consuming and ineffective when new faults appear.

To identify faults, some methods associate a particular signature with an out-of-control event. Each out-of-control event must be compared with a set of signatures to identify its origin. Considering the great number of candidate signatures, it is difficult to apply this method in real time. Engineer subjectivity can also be a source of errors, because the relationship between the signature and a multidimensional observation often is not clear.

Because no single control solution provides adequate results, a combination of different solutions is needed. Real-time control (starting with run-to-run and wafer-to-wafer controls) requires automatic forward or backward error correction. But an automatic correction system presupposes the existence of automatic fault detection and classification. Fault detection must be able to function as a plug-in module that fits the central APC system and is easy to reuse. A modeling application necessarily has limited relevance unless an efficient fault identification system is established. On the other hand, only MBPC can efficiently automate the correction of anomalies.

Global process control was designed to carry out these multiple tasks. A subtle combination of multivariate SPC, MBPC, and mathematical procedures, GPC combines the advantages of all these methodologies while avoiding their inherent drawbacks.

The use of multivariate control charts for fault detection is essential for extracting relevant information from the large amounts of data generated by equipment and processes. This method reduces the frequency of irrelevant alerts and detects faults that are invisible on common SPC charts. Moreover, multivariate methods solve the problem of fault identification. Because the origin of a fault is identified in real time, fab personnel can take corrective action quickly and limit rejected product.

GPC is performed by following a sequence of steps. First, a GPC error detection chart is created on the basis of Hotelling's T2 multivariate SPC methodology. This chart enables engineers to monitor all error occurrences in a single chart and takes new measurements into account.

Next, the GPC methodology adds signatures to already known errors. These signatures are linear tracks on the T2 chart. The establishment of signatures makes it possible to determine whether an error fits an already known error type or whether it is a new type. Figure 1 shows error signatures in a two-dimensional T2 control chart for two detected errors. The x- and y-axes depict two different variables, both of which have absolute values. The red circles represent one type of error, the red triangles the other; the blue crosses correspond to controlled measurements. The ellipse is the graphical representation of the T2 control limit. The numbered plots represent products. This figure shows how linear tracks can be represented.

To show the intensity and the variability of an error, the next GPC step is to create a specific error control chart for each error signature, which enables engineers to monitor error types that have already been encountered.

GPC then uses a factorial methodology to determine the correlation of each variable to these signatures (or the impact of these variables on the signatures). This step has two major advantages: it automatically determines the origin or cause of errors and also provides a model to determine the impact of each variable on the errors, enabling engineers to determine which parameters have to be modified to maintain a controlled process.

In the event of a new error type, the linear track or signature of the newly encountered error is detected automatically. As illustrated in Figure 2, an associated specific error control chart is then created for this new error signature. This two-dimensional figure, an extension of the data in Figure 1, demonstrates that new measurements will either be in control (inside the ellipse) or out of control (outside the ellipse) and that the out-of-control measurements will appear either on the existing linear tracks or on other linear tracks. Each linear track corresponds to an error type.

Figure 2: Two-dimensional chart extending the data in Figure 1. The new (larger) measurements can either be in control (inside the ellipse) or out of control (outside the ellipse). Out-of-control measurements appear on the existing linear tracks and on other linear tracks; each linear track corresponds to an error type.

Figure 3: Control chart for two different variables (on the x-axis and y-axis, respectively) indicate whether a measurement should be viewed as in control or out of control in the T2 calculation; 95% of controlled variables fall within the small ellipse and 95% of uncontrolled variables fall within the large one.

GPC's probabilistic approach to factorial analysis can discriminate between variables by identifying whether a variation associated with a given measurement is exceptional or ordinary. GPC also provides important information on which variables are anomalous. In Figure 3, a control chart for two different variables (on the x-axis and y-axis, respectively) highlights specific problems. These data enable engineers to determine whether a measurement should be viewed as out of control in the T2 calculation. The truncation limits in the figure represent measurement points that are outside the control limits.

By carrying out the different GPC steps, engineers can completely automate the detection and identification of errors. Using this method, models can be created so that when errors are detected, corrective action can be performed manually after an alarm is sounded or automatically with the aid of the obtained models.

Implementing GPC on the Fab Floor

To determine GPC's process control capabilities, a test involving a chemical vapor deposition (CVD) tool was conducted at STMicroelectronics. A SilverBox from Si Automation provided equipment and sensor integration with the CVD tool and performed data collection. To obtain models for automatic fault detection and identification, the GPC Scan software module analyzed data used for automation and data collected during processing.

To implement automatic fault detection, classification, and identification (FDCI), information was first collected by the SilverBox through its equipment, peripheral, and sensor interfaces, as well as from its automation part. The SilverBox then prepared this information for the GPC methodology by transforming it into homogeneous data following Gaussian laws.

Automatic FDCI was obtained from this rescaled data by using the SilverBox's GPC Guard. The GPC Guard used the results obtained by the GPC Scan and was responsible for fault detection by calculating the T2, for fault identification by classifying the error signatures, and for launching corrective action. The GPC Scan also prepared models for the GPC Guard. On historical data, it carried out fault detection by calculating the T2, fault classification by identifying the error signatures, and fault identification by iterative factorial analysis.

Figure 4: Global control chart for error detection enabled engineers to monitor errors globally while minimizing undetected errors and false alerts.

Figure 5: Factorial control chart of two variables before error classification. This chart enabled engineers to determine whether measurements should be considered in control or out of control in the T2 calculation step. The ellipse corresponds to the control limit and truncation limits show out-of-range measurements.

Initial Univariate Analysis. The test was based on the GPC analysis of 11 variables collected during the process. Initially, 11 univariate SPC control charts of the monitored variables were prepared in which the variables were scaled on normal law for easier interpretation. Each chart displayed the absolute values of a given variable over a series of wafers and lots. The center of each graph showed the average of all the measurements for the given variable.

Fault Detection. A T2 control chart was then built for the 11 variables, which enabled the engineers to monitor errors globally while minimizing undetected errors and false alerts. Figure 4, the global control chart for error detection, displays the wafer numbers on the x-axis and the distance of the measurements from the center of the controlled distribution on the left-hand side of the y-axis. The control limit was set up to 5% (associated probabilities are shown on the right-hand side of the y-axis). Variables in the range of 65 and above are shown on the truncation line.

Fault Classification. Next, the GPC Scan performed an iterative factorial analysis to classify errors by isolating presumed out-of-control observations at each iteration. Figure 5 is an example of a factorial control chart for two variables before error classification. The chart shows one compiled variable as a function of another compiled variable. The ellipse corresponds to the control limit and truncation limits show out-of-range measurements. This and other factorial control charts enabled engineers to determine whether measurements should be considered in control or out of control in the T2 calculation step.

The iterative factorial analysis step led to the error classification step. As shown in Figure 6, the iterative process allowed engineers to classify errors to obtain a stable classification. At first, each iteration resulted in a new error class. But during the fifth iteration, two classes were gathered into one when the iteration determined that they belonged to the same class. Other error classes were then determined, after which three of them were gathered into one and split again into two. Since subsequent iterations did not produce different error classes, the engineers determined that the classification procedure was concluded. The result was six classes of errors.

Figure 7 presents a factorial control chart after error classification. As in Figure 5, this chart represents a compiled variable as a function of another compiled variable. Truncation limits are also shown. This time, however, all out-of-control measurements were determined. Most of them lay far from the controlled zone, which was much thinner than at the beginning of the iterative process. The out-of-control measurements that lay inside the controlled zone would have fallen outside it if the corresponding relevant compiled variables had been used to draw the chart. This chart is important because it shows that no controlled measurements lie outside the controlled zone.

Figure 6: Chart showing the classification step, which allowed engineers to obtain a stable classification.

Figure 7: Factorial control chart after error classification in which all out-of-control measurements, most of which lay far from the controlled zone, were determined. The chart shows error class INDHCA (the stabilized classification in Figure 6).

The error classification process led to the creation of specific-error control charts for each type of error. These charts were used to focus on a given anomaly. From the useless initial univariate control charts for individual variables, GPC Scan built control charts by error type. Apart from focusing on a given anomaly, these control charts differentiated between errors of the same type according to their intensity and variability.

Specific-error control charts for the first three detected error types in this test are presented in Figure 8. These charts show the distance of the measurements from the center of the controlled distribution. In each chart, the measurements correspond to the linear track of the specific error type; the results represent the distance of the measurements from the center of the distribution of the linear track in question. In each chart, blue crosses correspond to controlled measurements, red disks to the concerned error, and red circles to other error types. The x-axis shows the wafer number while the left-hand y-axis shows the absolute value of the distance from the center and the right-hand the associated probabilities of errors.

Fault Identification. Based on the factorial analysis, the GPC Scan then determined the origin of the detected errors for each of the six error classes. That step led to the creation of sensitivity charts for each detected error type. Figure 9 shows the sensitivity of each detected anomaly to the different variables. On the y-axis, 0 means that the variable had no impact—in other words, that it was not responsible for the type of error shown in the chart; 1 means that the error type was perfectly correlated to the variable. These charts typically show the profile of the different error types.

Now that a complete profile of errors existed, the GPC software was ready to interpret error origins. Figure 10, profiles of out-of-control variables, shows corresponding values of the variables for each wafer detected to be out of control. Wafers with the same error types were gathered together. Variables that fall between the red lines were in control.

Figure 10: Profiles of out-of-control variables showing corresponding values of the variables for each wafer detected to be out of control. Variables that fall between the red lines were in control. (MOYVCR = centered and reduced mean of variable.)

Decision Matrix Construction. In addition to factorial analyses, the sensitivity charts made it possible to create process control models, or decision matrices, which associated actions—whether alarms or automatic corrections—with identified errors. Each type of error had a signature, which was its linear track on the T2 graph and which corresponded to its sensibility chart. A decision matrix associates out-of-control action plans with each signature. Depending on the value of the measurements, these plans could be anything from alarms to no-action commands to a complete set of automated actions. Decision matrices allow engineers to control or correct errors automatically.

Applying GPC to Real-Time Control. After the GPC procedure, from the creation of the global T2 control chart to the construction of the decision matrix, had been performed off-line by the GPC Scan, it could then be applied to process tool control in real time. The GPC Guard software module uses the obtained process control models and decision matrices for run-to-run or wafer-to-wafer control. This reaction delay was chosen for the first versions of GPC Guard, even though GPC can theoretically be applied in real time. But compared with the huge delays for obtaining results from other available methodologies (days sometimes), run-to-run and wafer-to-wafer control is very close to real-time control.

Conclusion

Like any APC solution, GPC confronts the problem of implementation in the real world. Although many standards established by SEMI for equipment manufacturers and by Sematech for IC manufacturers regulate fab life, many fab areas remain beyond the scope of these standards, especially those that connect the SEMI world to the Sematech world. Consequently, the semiconductor industry has experienced several APC solutions, none of which has been sufficiently generic to offer an efficient solution for a broad range of applications.

Problems associated with implementing APC are manifold. How should relevant data be acquired and different data matched? On what platform should data acquisition and matching take place and how should the results be used? What type of control system has the least impact on manufacturing and product yields?

The test discussed in this article demonstrates that the GPC approach can offer a solution to many APC-related problems. While the test was performed in a pilot project for a CVD application, the results demonstrated that GPC could identify all out-of-control variables as the causes of equipment or process deviations. The use of the GPC classification tool permitted engineers to focus on relevant problems and ignore the signatures of second-order ones. For example, the signature of a first-wafer effect in dry etch, which was detected through GPC, could be automatically ignored in-line.

The flexible configuration possibilities of the data treatment phase of the GPC test adapted well to the customer's needs. Although the test was performed on dry etch equipment, the customer could quickly configure the GPC software to suit other types of tools.

This rapid reconfiguration capacity also allowed the customer to adapt GPC to the changing needs of the dry etch equipment. GPC was first applied to the critical phase of the dry etch process. After it proved its ability to detect and analyze all types of known errors in that phase, automatic corrective actions were integrated into the model, decreasing the need for human intervention. GPC also detected new error types, leading to its extension to all other process phases. For example, GPC detected and automatically corrected an error in the precritical phase caused by a problem in loading a particular gas. An error caused by an unloading problem in the last phase of the process also was detected and automatically corrected.

Although GPC allows real-time fault identification in an industrial context, automatic fault detection is not enough in the semiconductor industry; corrective action also must be taken. After identifying faults, GPC can automatically correct them. Actions taken to correct one fault can be used as a model to correct other faults of the same type. Most importantly, the GPC approach leads to the creation of corrective models based on the results of fault identification.

Martial Baudrier joined the central R&D department of STMicroelectronics (Crolles, France) in 2000. He is mainly involved in APC projects, especially in the field of multivariate approach developments. He received an engineer diploma in statistics and functioning safety from the Institute of Engineering Sciences & Techniques of Angers. (Baudrier can be reached at +33 476 925438 or martial.baudrier@st.com.)

Daniel Lafaye de Micheaux, PhD, joined Si Automation (Montpelier, France) in 2001 when the company acquired a GPC license. He is the company's statistical and probabilistic expert and has been recognized for his contributions to quality and manufacturing. A professor at Sofia Antipolis University (Nice, France), Lafaye de Micheaux patented GPC in 1998. He received a PhD in statistics and has researched and taught in that field for more than 25 years. (Lafaye de Micheaux can be reached at +33 466 807288 or daniel.ldm@siautomation.com.)

François Lemaire joined Si Automation as product manager in 2000. Previously, he conducted research in R&D process improvements in the fields of pharmaceuticals and aeronautics. He received a diploma from the HEC School of Management (Paris) and has specialized in statistics, probabilities, and econometrics. (Lemaire can be reached at +33 467 033700 or f.lemaire@siautomation.com.)