Controlling etch tools using real-time fault detection and classification

Mao-Shiung Chen and T. F. Yen, ProMOS Technologies; and Barry Coonan, Straatum

Semiconductor manufacturers use process control methods and an analysis of tool sensor outputs to improve yields, increase tool productivity, and reduce manufacturing costs. Statistical process control (SPC) utilizes statistical algorithms to detect excursions. In contrast, this article presents a novel fault detection and classification (FDC) approach from Straatum (Dublin, Ireland) that is based on a pattern-recognition algorithm. Outputting a chamber-status metric known as the plasma index, this real-time FDC method is in place at ProMOS Technologies (Hsinchu, Taiwan), a 200-mm manufacturing facility that runs a variety of semiconductor tools. Based on a number of case studies, this article discusses the use of Straatum's FDC method in conjunction with several DRM oxide etch tools from Tokyo Electron (Tokyo).

FDC versus SPC

The behavior of plasma etch tools is difficult to control because small variations in equipment and process setup can have an unpredictable impact on etch quality. Many parameters have a very small margin of deviation (<1%) before yields decline. Historically, methods to control etch processes have relied on end-of-line metrology and short-loop test wafers. While these methods have been used to detect process chamber faults, they are slow and sometimes provide an unrealistic picture of etch chamber behavior. Determining the root cause of chamber faults has relied on engineer experience and interpretations of tool data—clearly not the most efficient way to detect and remedy tool faults. Given the wealth of data provided by process tools, more-sophisticated methods have been developed, which are known collectively as advanced process control (APC).^1,2

Using SPC, engineers can systematically interpret data from many tool sensors using statistical rules that can track tool performance and detect fault conditions in real time. This technique can be used to analyze data in univariate or multivariate modes, in which excursions are defined as
deviations from a statistical mean outside the normal limits of variation. A drawback to this method is that the tool parameters in question often do not display a normal distribution, so that such concepts as average and standard deviation can only be approximate. This uncertainty can lead to compromises that broaden fault limits, reducing sensitivity, or that allow benign statistical outliers to be processed, giving rise to false alarms.

The FDC methodology at ProMOS uses a pattern-recognition algorithm to provide a real-time interpretation of the state of the etch chamber.³ This approach overcomes the limitations of standard SPC, since it does not assume a priori knowledge of the statistical nature of the data. By learning the characteristics of a chamber fault using tool data, engineers can create a library of fault patterns.

A fault library is generated using data from a design of experiment performed on the chamber. There is no limit to the number of faults that can be added. When a wafer is run in the chamber, a fingerprint, or data pattern, associated with the data received from tool sensor outputs is recorded and compared with the patterns in the fault library. When a match is found between the wafer fingerprint and an entry in the fault library, a verdict can be reached as to whether the chamber is in a fault state and what the most probable root cause of the fault is. The go/no-go chamber decision is presented as a single value—the plasma index. If the plasma index is greater than 1 or less than –1, the tool is in a fault condition.

Even if a wafer is statistically outside the defined variation limit of a particular measured parameter, its fingerprint must first match a known fault before an alarm is triggered. Thus, normal sensor data variations do not trigger false alarms, and the number of false alarms caused by benign outliers is reduced.

Plasma Impedance Sensor

The quality of an etch process is principally driven by the condition of the plasma: Changes in chemistry resulting from variations in the gas-flow rate, the deterioration of chamber parts, pressure deviations, or power or electrode gaps all affect the plasma and, therefore, etch characteristics. Nonintrusive measurements of the plasma state from instruments such as impedance sensors or optical emission spectroscopes can provide rich information that can be used for process monitoring. In the FDC technique discussed here, the pattern-recognition model is based on data from an impedance plasma sensor from Scientific Systems (Dublin, Ireland), although the method can use data derived from other tool sensors or combinations of sensor data.

An impedance sensor measures the fundamental and harmonic voltage, current, and phase of the radio-frequency power signal to the chamber. Positioned between the match and the chamber, it can detect changes in plasma condition without interference from the match or other components in the transmission line. The sensor does not affect process characteristics and has the same form factor as the tool component that it replaces at installation.

Figure 1: Impedance sensor measurements of the magnetic interaction of twin etch chambers. The measurements were taken over a time period of approximately 60 seconds.

The sensitive measurement data from an impedance sensor presented in Figure 1 demonstrate that the magnetic fields of the two adjacent chambers in an etch system interact with each other and affect the system's impedance. When only one chamber magnet rotates, the character of the impedance fluctuation changes. While the plasma is affected by the orientation of the chamber magnets, this phenomenon has no impact on etch performance.

Figure 2: Fundamental voltage measurements from the etch tool taken over several preventive maintenance cycles. The red vertical lines mark when preventive maintenance actions occurred.

Figure 2 shows how preventive maintenance (PM) cycles performed on the etch-tool chamber influence the tool's fundamental voltage signal when measured at 13.56 MHz. Although PMs cause the tool to exhibit nonnormal behavior, their impact is benign. Hence, a statistical approach to understanding them would be inefficient, leading to a compromise between sensitivity loss and false alarms. In contrast, the FDC approach used at ProMOS learns only those patterns associated with faults and thus ignores data from events such as PM cycles.

Figure 3: Schematic diagram of the tool-monitoring data flow.

Plasma Index Monitoring

As illustrated in Figure 3, data acquired from the sensor are sent to a Straatum real-time (SRT) controller, which uses ImPrint MX software. After acquiring data from the sensor, the software tests the wafer fingerprint against the fault library using the FDC algorithm and calculates the plasma index. The SRT has a connection to the tool/host SECS stream to gather data-labeling information and a network connection through which data are sent to a central fab server to permit factorywide access.

Figure 4: Software engineering view illustrating that wafers processed when the chamber was in a fault condition lie outside the plasma index alarm limits (central shaded band).

Users have access to the software via a touch screen at the tool. Information available to equipment operators includes whether or not the tool is in a fault state, what the probable root cause of a fault is, and a list of wafers affected by the fault. Engineers have access to more-detailed information on recent wafers run on the tool, including a calculated multivariate analysis index and raw sensor data, as presented in Figure 4. A Pareto chart, as shown in Figure 5, is used to indicate the probable root cause of an excursion and the approximate magnitude of the parameter variation.

Figure 5: Fault classification Pareto chart showing the most likely root cause of an excursion (left-hand bar). The shaded bar indicates that the fault library parameter lies outside the alarm limits as defined by the user. That bar represents the most likely root cause of the failure.

Access to the fab server enables engineers to analyze data over a long period so that they can detect tool-behavior trends that may indicate fault conditions and compare data from different tools. Plasma index alarm limits can be defined by the engineer using the software, which also allows them to test the robustness of the FDC by archiving data to be tested along with the alarm limits. To set the plasma index limits accurately, the engineer must build the fault library so that it indicates how much variation is required for a parameter to trigger a fault condition that results in yield loss.

Figure 6: Software engineering view showing that affected wafers lie outside the plasma index limits and a Pareto chart (inset) showing the fault classification. The red plots represent the C5F8 flow deviation for these wafers. Because the fault impacts the ratio of O2 to C5F8, O2 is also reported to be out of spec. Once the C5F8 fault is rectified, the chamber returns to a good state for all parameters.

Using the FDC System

Case Study 1: C₅F₈ Gas-Flow Excursion. In the first case study, the plasma index classification indicated a chamber fault whose root cause was a C₅F₈ flow-rate problem associated with a mass-flow controller, as presented in Figure 6. The magnitude of the deviation was found to be approximately 5%, which was outside the safe limits as defined by the user and a potential source of yield loss. Subsequent end-of-line yield data, reflected in the wafer maps in Figure 7, showed this to be the case. Knowing the specific cause of the excursion enabled equipment engineers to respond rapidly, minimizing tool downtime.

Figure 7: End-of-line yield map indicating that wafers 1 and 2 (circled in red) experienced a yield-loss excursion that was detected and classified by the pattern-recognition algorithm.

In this case, the FDC system also indicated that O₂ flow was outside the normal process limits, as illustrated in the inset in Figure 6. This effect was a consequence of the C₅F₈ flow deviation, which caused O₂ partial pressure to shift and to operate outside safe limits. Once the C₅F₈ flow had been restored to the correct level, O₂ partial pressure returned to normal. This example demonstrates the importance of addressing the most significant, or left-most, parameter in the Pareto chart first, even if more than one root cause is indicated.

Case Study 2: Pressure Control Excursion. In the second case study, a yield-impacting excursion resulted from a malfunction in the chamber-pressure control system. Engineers replicated the excursion by reducing the valve angle at the chamber pumping port, thus increasing the chamber pressure. The plasma index detected and classified the fault correctly, calculating it to be close to 1.5 (i.e., a pressure deviation 50% greater than the alarm limit). Figure 8 presents the pressure data and Figure 9 classifies the excursion.

Figure 8: Data from a fault in the chamber-pressure control system (represented by the red plots) that was clearly outside the plasma index limit and, therefore, a potential yield limiter.

In general, when a fault is detected and classified, it is not necessarily the parameter control device but the particular chamber system that is determined to be the source of the problem. In this case, the fault, a damaged manometer cable, was correctly detected and classified by the equipment engineers. In other situations, the cause may be classified as a power fault while the actual faulty device may be the generator, the match, or a transmission line.

Figure 9: Classification of the excursion presented in Figure 8 shows that the root cause of the failure was a pressure fault. The red line is a user-defined confidence cutoff limit, below which the confidence level is too low to classify a fault accurately.

Conclusion

This article has dealt with an FDC system in a 200-mm semiconductor manufacturing environment. Unlike SPC methods, the FDC technique utilized here is based on a pattern-recognition algorithm that ignores nonnormally distributed chamber data. The system is therefore immune to drifts in sensor parameters caused by normal chamber cycling. The software and data presentation are user-friendly and intuitive, aiding tool operators as well as equipment and process engineers who perform troubleshooting operations. The data can be accessed throughout the fab over an internal network, enabling engineers to perform intensive analyses.

The examples of the technique presented here illustrate that real-time, accurate detection and classification of excursions can decrease tool downtime. Furthermore, the fault library can be expanded to include all process and chamber hardware faults, offering fab personnel a comprehensive detection and classification tool.

Acknowledgments

This article is based on a presentation given at the Taiwan Semiconductor Industry Association's Semiconductor Manufacturing Technology Workshop, held September 9–10, 2004, in Hsinchu, Taiwan.

References

1. C Fiorletta, "Capabilities and Lessons from 10 Years of APC Success," Solid State Technology 47, no. 2 (2004): 67–70.

2. TF Edgar et al., "Automatic Control in Microelectronics Manufacturing: Practices, Challenges and Possibilities," Automatica 36, no. 11 (2000): 1567–1603.

3. JV Scanlan, MB Hopkins, and K O'Leary, "Knowledge-Based Process Control for Fault Detection and Classification," Semiconductor Manufacturing 4, no. 10 (2003): 132–136.

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Mao-Shiung Chen is section manager of the etch department at ProMOS Technologies' Fab I (Hsinchu, Taiwan). In 1999 he received a BS in mechanical engineering from National Taiwan University of Science and Technology in Taipei. (Chen can be reached at +886 3 5798308 or maoshiung_chen@ promos.com.tw.)

T. F. Yen, PhD, is a senior manager of the Fab III etch department at ProMOS Technologies. From 2002 to 2004 he was manager of the company's Fab I etch department. He received a PhD in materials science and engineering from National Cheng-Kung University of Taiwan. (Yen can be reached at +886 3 5798308 or tf_yen@promos.com.tw.)

Barry Coonan, PhD, is a support engineer from Straatum (Dublin, Ireland) who is stationed in Taiwan. Previously, he worked on low-k chemical vapor deposition process development at Trikon Technology and the National Microelectronics Research Centre in Cork, Ireland, on the design, fabrication, and characterization of novel strained-silicon devices. He received a PhD in plasma physics from Dublin City University in Ireland. (Coonan can be reached at +353 1 8395122 or bcoonan@straatum.com.)