Optimization of Distribution Loop Filtration and Its Impact on a Copper CMP Process

Transcription

1 Optimization of Distribution Loop Filtration and Its Impact on a Copper CMP Process SEMICON West SEMI Technical Symposium: Innovations in Semiconductor Manufacturing (STS: ISM) Alex Pamatat*, Brian Bottema*, Keven Cline*, and Mike H.-S. Tseng** * Motorola Incorporated, 350 Ed Bluestein K3, Austin, Texas 7872 **CUNO Incorporated, 400 Research Parkway, Meriden, CT alex.pamatat@motorola.com mtseng@cuno.com Abstract A filtration scheme was designed and built to simulate a slurry distribution system and point of use (POU) slurry dispensing in order to evaluate filter performance in removing large particles from a colloidal silica based st step copper Chemical Mechanical Planarization (CMP) slurry. The major role of distribution loop filtration is to capture a portion of the large particles and protect the POU filter from premature plugging in order to deliver maximum filter service life. The filtration scheme was optimized based on large particle count (LPC) reduction and differential pressure across filter housings. Percent solids of CMP slurry samples were found to remain constant after filtration. For the existing distribution loop filtration scheme, polishing parameters including defectivity, copper removal rate, and filter usage were collected for patterned, production wafers over time to establish a baseline. The same polishing parameters were generated with the optimized filtration scheme for the slurry distribution loop. This work demonstrated the significance of the optimized distribution loop filtration on performance of the copper CMP process. Introduction Chemical Mechanical Planarization, is an enabling technology used in the production of complex semiconductor chips. CMP, which provides global and local planarization of wafer surfaces with micro-circuitry, is necessary to maintain precise circuit pattern details during the photolithography process. This becomes critical as line widths decrease and circuit levels increase. Killer defects from slurry contaminants have been linked to particles 0.5 µm at concentrations of ~50,000 counts per ml or less (). Filtration has been shown to be effective in removing large particles from CMP slurry, resulting in a reduction of micro-scratches on the polished wafer surfaces (2-5). Hence, filtration has been integrated into the CMP process for better yield management in manufacturing IC devices. To run a stable CMP operation, it is highly desirable to eliminate unscheduled filter changes, reduce the number of filter changes, improve process control, and lower the cost of tool ownership. In this work, a laboratory filtration test stand was set up to simulate both the distribution loop and point of use (POU) slurry dispensing in order to evaluate filter performance. Using results generated from laboratory testing, a filtration scheme was recommended for production trials. The objective of this work was to optimize the distribution loop filtration and to examine its impact on the performance of a copper CMP process regarding defectivity, copper removal rate, filter change-out, and cost of tool ownership. Experimental Filter and Slurry CUNO OPTIMA CMP filters were first tested in the laboratory to determine an optimized filtration scheme for subsequent production trials. Colloidal silica CMP slurry formulated with a peroxide-based oxidizer was obtained from Motorola MOS3 for filtration evaluation. The slurry is designed for a st step copper (Cu) CMP process for Cu removal and has a ph of approximately 8.5. Filtration Protocol To mimic the actual filtration process in the fab environment, multi-stage filtration was performed with two stage distribution loop filtration and single stage POU filtration as shown in Figure. Diaphragm Pump Ball Valve Distribution Loop Filtration POU Filtration Figure Schematic of slurry distribution and point of use filtration Metering Valve Once the filtration test stand was set up to simulate CMP slurry distribution in a slurry distribution tool and POU dispensing, a filtration protocol was followed as described below. () Introduce fresh de-ionized (DI) water to the empty test system at a desired flow rate. To bleed air from the system, close the downstream valves on both the recirculation loop and the point of use leg. Open the vent port on the first filter housing in the recirculation loop. Wait until steady stream of DI water passes through the vent port. When all air has been bled from the housing, close the vent port. Repeat the process on the second filter housing in the recirculation loop. When all the air has been bled from this housing, repeat the process on the point of use filter housing. Open the valves downstream of both the recirculation loop and the point of use filter housing completely. Any stored pressure will pass - -

2 through the system. Ensure effluent from the point of use housing is directed to the recirculation tank as well. Rinse empty system with DI water for a minimum of one hour to flush out any unwanted contaminants. (2) Drain DI water from the system and install 0.5 µm CUNO OPTIMA clean-up filters in both housings within the recirculation loop. Introduce fresh DI water to the system at a desired flow rate. Bleed system of excess air as described in step (). Recirculate DI water at the desired flow rate, for a minimum of one hour to further clean up the test system (up to a maximum of 24 hours if the system has been stagnant for several days). Drain DI water from system. (3) Install desired test filters (three) into appropriate housings. Introduce fresh DI water to the test system at a desired flow rate. Bleed system of excess air as described in step. (4) Ensure thorough wetting of each filter media. This is a two-step process as follows: (a) Close the valve downstream of the point of use filter to restrict flow solely through the recirculation loop. Next, limit the flow through the recirculation loop by closing the valve downstream of the test filters in this loop, until a back pressure of between psi (across each filter within the recirculation loop), is attained. Maintain the desired flow rate while recirculating DI water for 0 minutes to ensure sufficient wetting of the filter media. (b) Open the valve downstream of the point of use leg completely. Regulate the flow through the recirculation loop by adjusting the valve on this leg such that a pressure ranging between psi is achieved across the point of use test filter. Flow through the point of use filter is unregulated; allow DI water to recirculate for 0 minutes to ensure sufficient wetting of filter media. Drain system of all DI water. (5) Introduce slurry to the system at a pump speed required for the desired flow rate. Bleed system of excess air as described in step. Each pressure gauge will now display the true starting test pressures. Record each pressure. Pump slurry through the recirculation loop of the system at the desired flow rate, directly from a covered vessel containing 30 liters of non-agitated slurry. Flow through both the recirculation loop and the point of use filters should be regulated via the valves downstream of the recirculation loop and the point of use filters, to maintain the desired recirculation flow rate and POU flow rate. (6) Collect 25 ml influent and effluent samples at the recirculation loop and at the point of use filter. Caution should be taken to ensure sample vials are completely filled (trapped air promotes slurry drying, which leads to LPC s). Times of sample collection are as follows: (a) distribution and POU filtration: hr, 3 hr, 5 hr, 24 hr (b) Influent: 0 hr (prior to test start), 5 hr, 24 hr. Record the test pressure across all filters for each sampling interval. (7) Halt the introduction of slurry to the system, but continue to pump at the desired flow rate until the remaining slurry is exhausted from the system. Then introduce fresh DI water at the desired flow rate. Bleed any excess air from the system as described in step. Flush DI water through the system, and allow it to go to the drain for a minimum of 5 minutes, or until the color of the effluent becomes clear (an indication that the bulk of slurry has been removed from the system). (8) Drain the system of DI water and remove the test filter. Two 0.5 µm CUNO OPTIMA filters are then installed into both housings to clean up the recirculation loop. Introduce fresh DI water to the system at the desired flow rate. Bleed the system of any excess air as described in step. Flush fresh DI water to the drain for a minimum of 5 minutes and then recirculate DI water throughout the system for a minimum of 20 minutes. (9) If another trial is not immediately scheduled, one of the following two options should be implemented: a) If the system is to be inactive for a short period of time (maximum of 48 hours), reduce the flow rate to minimize wear and tear on the pump. Continue recirculation until ready to begin next trial. Then, repeat steps 3-9. b) If the system is to be inactive indefinitely, close the valve downstream of the test filter. Allow the operating pressure across the filter to increase to 30 psig to prevent any residual slurry from drying and then stop the pump. Maintain the system in this pressurized state until prepared to begin the next trial. Repeat steps -9. Particle Sizing Analysis Particle size analysis was performed for influent and effluent samples collected at different filtration times via Accusizer Model 780A by Particle Sizing Systems. The LPC 0.56 µm were reported and used for computing filter efficiency. Percent Solids Determination The concentration of silicon (Si) in solution was determined using Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP AES). The amount of silicon (Si) present was converted to silicon dioxide (SiO 2 ) via appropriate calculations. Wafer Polishing The selected filters for distribution loop filtration were installed in the slurry distribution system at Motorola s MOS3 facility. The differential pressures across the distribution loop filters were monitored and used as references. Distribution loop and POU filters were typically changed out based on a weekly and bi-weekly preventive maintenance (PM) schedule, respectively. Flow rates for slurry priming and production runs were kept the same throughout the distribution loop and POU for both CUNO and the incumbent competitive filters. No changes were made to the distribution loop back pressure. After POU filtration, the effluent was supplied to the polishing tools for wafer polishing evaluation. All polishing data was obtained by processing 200 mm substrates with 0.3 µm line width dimensions on an Applied Materials Mirra tool at Motorola s MOS3 facility. Defect scan measurements were taken on an Applied Materials Compass and a KLA 238, KLA defect classification was completed on a SEMVision. All data and comparisons were collected on a process using the same stacked IC type - 2 -

3 urethane pad, diamond conditioner disk, and commercially available colloidal silica slurry for each process run. Flat film wafers with a 0 ka copper film on top of a TEOS base layer were used for all of the copper removal rate tests. Copper film thickness measurements were made with a Tencor RS75. Results and Discussion Particle Size Distribution for 0.56 µm Before and After Filtration To optimize the distribution loop filtration, several multistage filtrations were performed using the laboratory filter test stand. Particle removal 0.56 µm is summarized in Table for CUNO OPTIMA filters. Table Summary of Particle Removal 0.56 µm fordistribution Loop Filtration Particle Removal 0.56 µm CUNO OPTIMA. Filtration Time, hr 0 µm/5 µm 5 µm/3 µm 5 µm/ µm In the distribution loop, the filtration scheme which employed a 5 µm pre-filter, followed by a 3 µm final filter demonstrated a higher LPC reduction than the scheme consisting of a 0 µm pre-filter and a 5 µm final filter. Although the LPC reduction was further improved by the filtration scheme consisting of a 5 µm pre-filter and µm final filter, the differential pressure across the µm filter was elevated, raising the potential for pre-mature plugging. Therefore, the filtration scheme consisting of the 5 µm prefilter and 3 µm final filter was recommended for production trials. Percent Solids The percent solids of both the influent and effluent were sampled at, 3, 5, and 24 hour intervals and then analyzed by ICP. Several atomic absorption silicon (Si) standards were prepared and used to establish a calibration curve. Relative changes in percent solids for the effluent samples were determined to be within 5 % of the unfiltered CMP slurry. These changes are within the experimental uncertainty for measuring Si content via ICP (4). Therefore, the CUNO and competitive filters did not alter the percent solids of the CMP effluent. Hence, the polishing rate is expected to remain unchanged with the use of either CUNO or the competitive filtration scheme. Wafer Polishing Performance In the wafer polishing performance evaluation, the polishing data collected for the competitor 0 µm prefiltration and 5 µm final filtration was used as a baseline for comparison. Normalized copper removal rate data is presented in Figure 2 for CUNO and the competitive filtration schemes. Bulk copper removal was completed on platen while optical endpoint and overpolish were performed on platen 2. The copper removal rate data was measured using 6 mm edge exclusion and 30 measurement sites were taken per wafer. Polishing rate data is normalized to the total mean of the rate for each platen. Each data point represents the average of 30 measurement points for a single wafer and each data set consists of month of data as shown in Figure 2. The removal rate of copper remained unchanged when CUNO and the competitive filtration schemes were used for the distribution loop. Therefore, there was no change in the copper polishing rate when the OPTIMA filtration scheme was employed. Oneway analysis of the normalized Compass total defect counts are presented in Figure 3 for production loop A, B, and C. Each production distribution loop supports a number of polishing tools. Baseline defect data was collected using the competitive 0 µm pre-filter and 5 µm final filter in the distribution loop followed by a 0.3 µm POU filter at the polishing tool. To evaluate the impact of the optimized distribution loop filtration on the CMP process performance, CUNO s OPTIMA 5 µm and 3 µm filters were installed in Distribution loop B, the incumbent POU filtration level remained at 0.3 µm. Defect data was collected by polishing a large number of wafer lots including 497, 270, and 207 for the competitor s filtration scheme whereas 9 lots were used for CUNO s OPTIMA filters; 4 wafers per lot were used for defect analysis. In Figure 3, each defect data point represents the average of 4 wafers from a given lot. All defect count data is normalized to the median of the CUNO data set. Distribution loop A displays a significant variation in defect count. This probably illustrates the variation from polisher to polisher as well as the large sample size for this loop. Since testing for normality proved each dataset distribution to be not normal, t- test was not used and means comparisons of normalized defect counts was not performed. To get the same baseline for comparison, distribution loop B was used for testing OPTIMA filters and the incumbent competitive filters. The normalized defect count data for production loop B is presented in Figure 4 for CUNO and the competitive filters. The CUNO OPTIMA filtration scheme resulted in an apparent shift to lower total defect counts as presented in Figure 4. However, the distribution of defect count data is not normal even after transformation using the formula of (normalized defect count) , and therefore, t-tests are not appropriate making it impossible to perform meaningful statistical analysis of the defectivity impact by the optimized filtration versus the incumbent filtration schemes. Figure 5a is a random adder defect density control chart from inline defect monitor scans on a high volume 0.3 µm production part. The random adder defectivity includes all killer defectivity types (i.e. killer scratches, non-killer scratches, killer plate block, killer missing pattern, killer residual metal, etc.). 20% of the total volume of this 0.3 µm material is scanned at pre-determined process stages, post metal 4 CMP happens to be one of these process stages. Examining Figure 5b, we can see that the outlier data point in - 3 -

4 Figure 5a is due to excessive killer plate block defectivity. The root cause for plate block defects is most often associated with unit processes within the same module (i.e. metal layer) but prior to and independent of CMP. Excluding the outlier data point, a marginal improvement is seen in random adder defectivity by using the optimized filtration scheme. The impact of optimizing the distribution loop filtration on copper CMP performance can be best illustrated by analyzing monthly POU filter usage. Prior to implementing the OPTIMA filtration scheme, it was difficult to maintain the target POU filter change-out on a bi-weekly basis. This is because the 0.3 µm POU filter offered a service life anywhere between 2 hours and 4 days due to random flow decay and filter plugging which in turn required unscheduled change-out for the POU filter. With this filtration scheme an average of 66 POU filters were used each month. After implementing CUNO s OPTIMA filtration scheme, the useful life of the 0.3 µm POU filter was extended to beyond 28 days. Due to the increase in filter lifetime from using the OPTIMA filtration scheme, Motorola was able to reduce their POU usage to 42 filters per month. According to this study, a monthly PM was instituted for the POU filter by using the OPTIMA filtration scheme. Figure 6 clearly shows a downward trend in the usage rate after implementing this filtration scheme. It should be noted that the data is convoluted by tool soft idling, tool troubleshooting, scheduled slurry distribution loop flushes and the fact that multiple process areas (i.e. Copper, Tungsten, etc.) use the same POU filter, which results in an actual POU filter usage rate that deviates from the calculated 42 filters per month. Conclusions An optimized filtration scheme was determined based on LPC reduction and differential pressure via laboratory filtration testing using CUNO OPTIMA CMP slurry filters. The recommended filtration scheme was then implemented in the production distribution loop and the results demonstrated that the copper CMP process control was improved. Furthermore, unscheduled POU filter change outs were eliminated and the POU filter lifetime was doubled. Thus the cost of ownership for the process was reduced while maintaining the same PM schedule for the distribution loop filters and extending the lifetime of the POU filters. The OPTIMA filtration scheme resulted in an apparent downward shift to overall wafer level defectivity. Conference, Proceedings, February 9 2, 2003, Marina Del Rey, CA, p Westbrook, J.; Li, Y.; Tseng, H.-S.; Evaluation of Point of Use Filtration Systems for Copper CMP Slurry, 5 th International Symposium on Chemical-Mechanical Polishing, Center for Advanced Materials Processing, August 4 6, 2002, Lake Placid, New York. 3. Tseng, H.-S.; Carter, K.; Marchese, J.; Parakilas, M.; Arefeen, Q.; Hackett, T.B.; Hymes, S.; Proper Filtration Removes Large Particles from Copper CMP Slurries, Proceedings of Eighth International CMP-MIC Conference, February 9 2, 2003, Marina Del Rey, CA, p Tseng, H.S.; Proper Filtration Removes Oversized Particles from CMP Slurry Systems, Proceedings of the SEMICON China 2003 Technology Symposium Semiconductor Equipment and Materials, March 2-4, Shanghai, China, p Johl, B.; Manzonie, A.; Dynamic Pot-Life and Handling Evalution of EPL2362 First Step Copper Slurry, Eighth International CMP-MIC Conference, Proceedings, February 9 2, 2003, Marina Del Rey, CA, p Acknowledgments The authors would like to thank John Morby for performing bench top filtration experiment and Mike Parakilas for determining percent solids of slurry samples. Reference. Nicholes, K.; Litchy, M.R.;Hood, E.; Easter, W.G.; Bhethanabotla, V.R.; Cheema, L.; Grant, D.C.; Analysis of Wafer Defects Caused by Large Particles in CMP Slurry Using Light Scattering and SEM Measurement Techniques, Eighth International CMP-MIC - 4 -

5 Figure 2 Copper Removal Rate Oneway Analysis of Normalized Platen Rate by Filter Brand.2 Normalized Platen Rate Oneway Anova Summary of Fit CUNO 5 um/3 um Rsquare Adj Rsquare Root Mean Square Error Mean of Response Observations (or Sum Wgts) t-test Estimate Std Error Lower 95% Difference t-test 0.74 DF 29 Competitor Prob > t Assuming equal variances Analysis of Variance Source DF Sum of Squares Mean Square Filters Error C. Total Means for Oneway Anova Leve Number Mean Std Error l CUNO Competitor Std Error uses a pooled estimate of error variance Means and Std Deviations CUNO Competitor Number 06 5 Mean Std Dev Lower 95% F Ratio Prob > F Std Err Mean Lower 95% UCL TGT LCL LCL Oneway Analysis of Normalized Platen 2 Rate by Filter Brand.3.2 Normalized Platen 2 Rate Oneway Anova Summary of Fit CUNO 5 um/3um Rsquare Adj Rsquare Root Mean Square Error Mean of Response Observations (or Sum Wgts) t-test Estimate Std Error Lower 95% Difference Assuming equal variances Analysis of Variance Source Filters Error C. Total DF Number CUNO 05 Competitor t-test Sum of Squares Means for Oneway Anova Mean DF 29 Competitor Prob > t Mean Square Std Error Lower 95% F Ratio.097 Prob > F UCL UCL TGT TGT LCL LCL Std Error uses a pooled estimate of error variance Means and Std Deviations Number Mean Std Dev Std Err Mean Lower 95% CUNO Competitor Figure 3 Normalized Defect Count Data Comparison for Production Loop A, B, and C (Metal 4 Compass Data) Oneway Analysis of Normalized Defect Count (to CUNO 5/3) By Filter Brand Normalized Defect Count Baseline Loop A Competitor Filter Quantiles Baseline Loop A Baseline Loop C CUNO 5 um/3 um Loop Means and Std Deviations Baseline Loop A Baseline Loop C CUNO 5 um/3 um Loop B Competitor Filter Minimum Number % Mean Baseline Loop C Competitor Filter 25% Std Dev Median Std Err Mean CUNO 5 um/3 um Loop B 75% Lower % Maximum

6 Figure 4 Normalized Defect Count Data Comparison for Production Loop B - Metal 4 Compass Data Oneway Analysis of Normalized Defect Count (to CUNO 5/3) By Filter Brand 3 Oneway Analysis of Normalized Defect Count (to CUNO 5/3) By Filter Brand 3 Normalized Defect Count 2 After transformation Normalized Defect Count 2 Baseline Loop CUNO 5 um/3 um Loop B Baseline Loop CUNO 5 um/3 um Loop B Quantile CUNO 5/3 um Loop B Minimum % % Median.92 75% % Maximum Quantile Figure 5a Normalized Defect Density Production Data KLA/SEMVision ILM RADO Control Chart CUNO filters installed Metal 4 ILM Data /8/03 07/08/03 07/28/03 08/7/03 09/06/03 09/26/03 0/6/03 Minimum 0% CUNO 5 um/3 um Loop B Means and Std Number Mean CUNO 5 um/3 um Loop B % Median % % Maximum Std Dev Std Err Mean Lower 95% RADO SEMVision review showed defectivity to be killer plate block and killer voiding; not filter related. Date - 6 -

7 Figure 5b Normalized Killer Plate Block Production Data Metal 4 KLA/SEMVision ILM Control Chart Metal 4 Killer Plate Block Normalized Killer Plate Block High RADO data point from Figure 5a 06/8/03 06/25/03 07/02/03 07/09/03 07/6/03 07/23/03 07/30/03 08/06/03 08/3/03 08/20/03 08/27/03 09/03/03 09/0/03 09/7/03 09/24/03 0/0/03 0/08/03 0/5/03 0/22/03 Date Figure 6 Monthly POU Filter Usage Rate - including slurry flush usage- POU Filter Usage by Month including slurry flush usage- # of Filters X filter life time implementation Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Jan Feb - indicates a scheduled slurry flush was performed for the month Month - 7 -

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