Abstract

A new approach to monitoring molecular contamination in lithography is presented. Recent technical advances have made it feasible to perform continuous real-time monitoring with significant advances in sensitivity and stability while minimizing sample tubing effects. These improvements are realized by using a small, low-cost monitor that is dedicated to monitoring a single location. A dedicated, point-of-use monitor offers the following advantages over a conventional multipoint sampling system: continuous monitoring, no missed contamination events, sample tubing lengths reduced from 20 - 30 meters to 2 - 3 meters, and 5 - 10x better sensitivity. Improvements in sensitivity and stability are realized through a dedicated monitor approach to molecular contamination monitoring. Because the monitor is continuously sampling the same environment, sample averaging can be used in a highly effective manner to reduce the detection limit. This is particularly useful in chemically filtered environments where the concentrations are usually low and stable. An automated monitoring software package can simultaneously plot individual one minute data points and a long-term running average. The one minute samples are used to immediately detect the onset of a contamination event while the long term running average is used to monitor background contamination at the lowest levels.

Keywords: Molecular contamination, ion mobility spectrometry (IMS), AMC, ammonia, NH3, sulfur dioxide, SO2.

Introduction

A new approach to monitoring molecular contamination in lithography is presented. Recent technical advances have made it feasible to perform continuous real-time monitoring with significant advances in sensitivity and stability while minimizing sample tubing effects. These improvements are realized by using a small, low-cost monitor, the AirSentry II, that is dedicated to monitoring a single location. Previously, it has been cost prohibitive to conduct continuous monitoring of molecular contamination on a large scale.

Conventional molecular contamination monitoring systems employ a multi-point air sampling system connected to an analyzer. This monitoring approach has evolved into normal practice, driven by the need to monitor a large number of locations at the lowest reasonable cost. However, significant issues arise in today's 193 nm semiconductor fabrication environment, and the restrictive monitoring techniques currently in use begin to show their weaknesses. The biggest drawbacks of the conventional monitoring approach are:

• Small time period of actual and recorded measurements per day at each location
• Effects of long sample tubing lengths
• Analyzer response and clear down times when adjacent sample point concentrations differ greatly
• Minimal ability to perform sample averaging to increase sensitivity.

For example, a sixty point sampling system with a 10 minute sample cycle (9 minutes purge, one minute sample) will take 600 minutes or 10 hours to sample all locations. Each location will be monitored for 1 minute every 10 hours, leaving each location unmonitored for the remaining 9 hours and 59 minutes. Certainly quality control cannot be maintained with this monitoring methodology.

A dedicated, point-of-use monitor offers the following advantages over a conventional multipoint sampling system:

• Continuous monitoring
• No missed contamination events
• Sample tubing length reductions from 20 - 30 meters to 2 - 3 meters. Reducing the sample tubing lengths minimizes interactions between contamination molecules and the tubing surface.
• Sensitivity improvements of 5-10x over existing technology.

Improvements in sensitivity and stability are realized through the dedicated monitor approach to molecular contamination monitoring. Because the monitor is continuously sampling the same environment, sample averaging can be used in a highly effective manner to reduce the detection limit. In one application, for example, the limit of detection for one minute samples is 120 ppt and the limit of detection for a 60 minute rolling average is 8.2 ppt. This is particularly useful in chemically filtered environments where the concentrations are usually low and stable. An automated monitoring software package can simultaneously plot individual one minute data points and a long-term running average. The one minute samples are used to immediately detect the onset of a contamination event while the long term running average is used to monitor background contamination at the lowest levels.

Methodology

Ion mobility spectrometry (IMS)has been employed for decades as a robust technique for measuring traces of chemicals in the air and on surfaces. Although most commonly associated with airport security screening systems and chemical warfare detection systems, IMS has also been successfully applied to microcontamination issues faced in semiconductor, flat panel display, hard disk drive, pharmaceutical, and hard disk drive petrochemical industries. This paper presents the results of the AirSentry II IMS monitoring system which has been optimized as a system to meet the needs of photolithography engineers monitoring contaminants such as ammonia, total amines, and total acids.

IMS monitoring systems contain an IMS cell, a means to introduce sample for analysis, capability to process the cell output signal and the ability to mix selective ionization control reagents with the sample stream to suppress non-analyte ion formation. Most IMS cells contain an ionization source to generate ions, an electronic shutter to control the introduction of the ions into the separation or drift region, a high voltage supply to create an electric field gradient across the drift region, and an ion collector connected to an amplifier to generate a signal as a function of time. Analysis occurs at atmospheric pressure and one ion scan can be obtained in as little as 20 milliseconds.

When the ion shutter is pulsed open, a sample of ions is released into the drift region where they are accelerated toward the ion collector by the electric field. Ion species with different ion mobilities, K, will arrive at the collector at different characteristic drift times, td. Ion mobility can be empirically determined using the length of the drift tube, L, the voltage potential across the drift tube, V, and the drift time (1).

Thus, ions with a long drift time have a low ion mobility and ions with a short drift time have a high ion mobility. If assumptions are made regarding collisional momentum transfer and if it is assumed the ions have a Boltzmann-type distribution, collisions are binary, the energy of the ions is mostly thermal energy, and the ions are exposed to a low electric field, ion mobility can be estimated as follows: (2)

(download pdf for all tables and figures) (305.2 KB)

Where K is ion mobility; q, electronic charge; E, electric field gradient; N, total number density in drift tube; collision cross section area; m, mass of ion; M, mass of neutral molecule; k, Boltzmann constant; and T, temperature. An examination of equations (1) and (2) shows that, for a given IMS system, the size and mass of the ions or ion cluster is what allows them to be separated at characteristic drift times. These two unique properties allow an IMS cell to identify and monitor particular analytes of interest.

Optimization of monitoring system performance also includes the sample delivery system. The sample delivery system should be designed so there is minimal interaction between the delivery system and the air sample at the point of monitoring. Use of the shortest possible inlet lines and inert materials such as PFA or PTFE for tubing and connectors helps ensure rapid response times and accurate measurements. Potential surface interactions with the sample can be minimized by eliminating all valves, manifolds, and pumps upstream of the IMS cell.

Analytical specificity can be further enhanced through the addition of ionization control reagents to the sample stream. The ionization control reagents can suppress the formation of unwanted, non-analyte ion peaks, improving sensitivity and reducing potential interferences(2). Figure 1 shows an example of the use of ionization control reagents to measure ammonia in laboratory air. Even though many chemical species are present in the air, only the ion peak for ammonia and the ion peak for the reagent appear in the ion scan.

Figure 1: Use of ionization control reagents improve sensitivity and reduce potential interferences. In clean air, only the reagent ion peak is present. When ammonia is in the air sample stream, an ammonia ion peak is detected.

Data and Results

1. 1) Comparison between continuous and non-continuous monitoring

A typical manifold sampling molecular contamination monitor will select a sample line, purge for 9 minutes, perform a concentration measurement for 1 minute, and obtain one measurement point every 10 minutes. A comparison of the monitoring coverage from a 60 point manifold system, a 16 point manifold system, and a point-of-use monitor are shown in Table 1. As the number of sample points increases, the amount of time each day monitoring individual sample points decreases. In the extreme case of a 60 sample point manifold system, each sample point is monitored for an average of only 2.4 minutes per day. As shown in Figure 2 and Figure 3, long-term changes in molecular contamination will be observed with a manifold monitoring approach, but many shorter duration contamination events observed in continuous monitoring are not detected by a manifold system.

Table 1: Benefits of implementing a continuous point-of-use monitoring strategy.
(download pdf for all tables and figures) (305.2 KB)

Figure 2: Simultaneously tracking real-time data and running average data provides real-time event detection and very high sensitivity to detect longer term trends. Continuous monitoring with a point-of-use monitor ensures that no events are missed. With manifold systems, many transient events are not observed.

Figure 3: Continuous monitoring detects transient contamination events and tracks trends in low-level contamination levels. In this case, the background ammonia levels increase from 0.075 ppb to 0.170 ppb over a 7 day period. Two events were detected on day one. Simulated data from a 60 point manifold monitoring system is shown for comparison. It suggests an increase in ammonia concentration during the monitoring period, but because there are much fewer measurements, measurement noise makes it very difficult to accurately determine the magnitude of the concentration change.

Response and clear down time

Analyzer response and clear down times are important considerations when evaluating a monitoring system. The three largest factors influencing response and clear down times are: sample line length, the chemical compatibility of the wetted materials, and the age or exposure history of the sample line. As shown in Table 2, sample line length can affect response times. The data shown in Table 2 were obtained using new ¼" diameter PFA tubing. The observed effect can be much more pronounced with older tubing that has been exposed to high levels of contamination. For example, if low vapor pressure acids like sulfuric and phosphoric acids adsorb to the inner walls of a long sampling line, response times can become very poor for measuring low levels of ammonia. The response and clear down times of the AirSentry II ammonia monitor are shown in Figure 4.

Table 2: Sample tubing line length can affect analyzer response times. This data was obtained with new PFA tubing.

Figure 4: Rapid response and clear down can be obtained by minimizing air sample line lengths and careful selection of the "wetted" materials in the system.

Noise and detection limit considerations

This example illustrates an important issue to consider when evaluating monitoring systems. The detection limit of a monitoring system is almost always determined by the noise level, not by monitor's response to contamination or the resolution of the data output. Because most photolithography tools are very clean, near or below the detection limit of most monitoring systems, noise is an important factor in monitoring these environments. One useful metric of the noise in a monitoring system is the level of noise when monitoring a clean or zero air gas stream. n example of this noise measurement is shown in Figure 5. A monitoring system with a low level of noise will have low limit of detection. Generally, the detection limit is defined as 3*x, where x is the standard deviation of the measurement noise.

Figure 5: The standard deviation of the measurement noise determines the lower limit of detection of a contamination monitor. Generally, the limit of detection is considered to be 3x the standard deviation of the noise.

When considering monitoring continuously at the point-of-use or with a manifold system, it is important to evaluate the data from a statistical perspective. There is measurement error or noise associated with any measurement. Some sources of this error include: electronic noise, environmental noise, and surface chemistry effects. When the noise is random, the noise level is reduced by averaging multiple measurements. The standard deviation of a series of measurements is reduced by the square root of the number of measurements when the measurement variations are random. In this case, the measurement error for a single measurement can be 10 times higher than the average error of 100 measurements. The impact of averaging statistics on determining the concentration of molecular contamination in an area is shown in Figure 6 and 7. By using point-of-use monitoring, a greater number of measurements per unit time are provided, resulting in a reduction of measurement error and providing a more precise determination of contamination levels in a shorter time than is possible through a manifold monitoring system.

Figure 6: With continuous point-of-use monitoring, signal averaging provides significantly improved sensitivity.

Figure 7: Example of the gains in sensitivity that can be obtained through sample averaging.

Future Challenges

There have been significant advances recently in IMS analyzer technologies and chemistries. While this has improved detection limits and measurement stability, it has also created a new challenge. In the future, to fully utilize the capabilities of the new analyzer system, low-ppb and ppt-level calibration gases need to be generated. It can be very difficult to create stable ppt-level gas standards of reactive gases. A preliminary attempt at low-concentration gas standard generation for ammonia is shown in Figure 9. Compare the stability of the data at zero, one ppb, and two ppb. The data is noticeably less stable at one and two ppb than at zero. This suggests the instability is not due to the analyzer, but due to the calibration gas generation and distribution system at low concentrations. It is anticipated that surface interactions will be more significant at ppt level concentrations. A new calibration gas generation and distribution system is planned which will be optimized for low concentration calibration and testing.

Figure 9: Generating stable concentrations of reactive gas calibration standards at low-ppb and ppt levels is the new challenge in molecular contamination monitoring.

Conclusion

The optimal approach to monitoring molecular contamination in critical lithography areas is to have dedicated point-of-use analyzers which provide continuous, real-time data for each location of concern. This approach ensures that contamination events are not missed and it allows for substantial improvements in sensitivity through sample averaging. By locating the analyzer at the point-of-use, sample tubing effects are minimized. A new, low cost, high sensitivity IMS technology enables implementation of this continuous monitoring strategy. By using software to simultaneously track real-time contamination data and running average contamination data, a single monitor can both immediately identify contamination events and detect longer term shifts in background contamination levels on the order of 10 ppt.

Author

Dan Rodier, Particle Measuring Systems

Contact us if you need more information or have questions.

References

1. G. A. Eiceman and Z. Karpas, Ion Mobility Spectrometry, CRC Press, Boca Raton, 1994.
2. A. G. Harrison, Chemical Ionization Mass Spectrometry , CRC Press, Boca Raton, 1992

AirSentry® is a registered trademark of Particle Measuring Systems, Inc.