05898190

Advanced excursion control and diagnostics for CMP process monitoring Andrew Stamper Microelectronics Division IBM Corporation Hopewell Junction, NY 12533 Stamper@us.ibm.com

Abstract—This CMP processes are generally well known and established critical steps in semiconductor manufacturing, but must be very closely monitored and controlled to maintain process uniformity and minimize process induced defects. CMP processes are generally monitored using a combination of blanket test wafers, short loop patterned wafers and product wafers, along with a variety of in-situ controls. This paper talks about the techniques demonstrated for defect excursion monitoring in CMP module using short loop test wafers and an advanced dark Field inspection tool. These techniques enable effective control and monitoring of the CMP tools used for manufacturing advanced semiconductor logic devices. We talk about three approaches that helped accomplish this objective: (i) Pattern wafer inspection at M1 Cu CMP level using Puma 9550 DF inspector (ii) use of In-line Defect Organizer (iDO) to effectively and automatically extract and classify scratches – the key defect of interest to the CMP process engineer; and (iii) use of quick recipe templates to diagnose and make required recipe changes for 32 nm and 22 nm devices using ―Auto Derivative Recipe‖ feature on Puma 9550 to maximize engineering and tool efficiency Keywords-iDO(inline defect organizer), auto derivative,

I.

INTRODUCTION

This paper will discuss excursion control methodologies adopted at IBM using Puma 9550 DF wafer inspection system with Inline Defect Organizer for automatically classifying and monitoring scratches in the CMP module and Use of “Auto Derivative Recipe” feature to create multiple recipes to automatically monitor and diagnose issues due to small process changes on 32 nm and 22 nm product wafers. Having a functional, automated classifier will improve the Mean Time to Detect (MTTD) on the process tools and minimize product at risk to a potential tool or process issue. This paper will describe the methodology that enables fast and accurate defect detection and classification performance on Cu CMP short loop test wafers.

II.

Gangadharan Sivaraman, Ravi Sankar KLA-Tencor Gangadharan.Sivaraman@kla-tencor.com, Ravi.Sankar@kla-tencor.com

scratches) from other real defects (CMP residual slurry, Foreign Material, embedded contamination) and come up with a novel way to monitor scratches in CMP module and shut off CMP process tools based on results from iDO classification.

Figure 1: Concept of iDO for binning defects Performance of iDO is measured by accuracy on purity of a bin. The accuracy and purity of a bin is defined based on Confusion matrix.

INLINE DEFECT ORGANIZER

iDO provides a way by which the user can use the classified defect data from inspection system and try to separate different defect types. We were able to use Puma 9550 + iDO to successfully bin scratches (polishing and handling

978-1-61284-409-1/11/$26.00 ©2011 IEEE

Figure 2: Confusion Matrix

Accuracy is the Percent of defects agreed by ADC and manual classification relative to Number of Manually Classified defects in that Class and purity is the percent of defects agreed

by ADC and manual classification relative to number of Auto classified defects.

Automated tool monitoring strategy helped to flag bad CMP tools quickly

To represent them in confusion matrix Accuracy = diagonal element /column total Purity = diagonal element/row total In a simple manner Accuracy is a measure of efficiency of the classifier in identifying a particular class (for ex: class x), while purity is the measure of how many the computer called bin x are really class x.

The goal for using iDO effectively in production is to have high purity and accuracy when detecting defects to shutdown tools without going to SEM review. In the current CMP scratch monitoring strategy 80% purity and 95% accuracy was demonstrated and this was just right for implementing IBM’s automated tool monitoring strategy.

Figure 5: Wafer map indicated out of and in control lots flagged by the IBM automation system.

IV.

AUTO DERIVATIVE RECIPE

Auto derivative recipe creation is a method by which wafer inspector creates recipes automatically with minimum user input done prior to the wafer arrival at the tool.

The benefits of auto derivative recipe are tremendous for several reasons.

Figure 3: Confusion matrix showing good accuracy and purity for HS (Heavy Scratch) bin III.

AUTOMATED TOOL MONITORING

The excursion monitoring is utilized on the CMP monitors and product in IBM’s 323 Fab. This allows the tool to classify in real-time the defects from the wafer maps and directly uploads to the Yield management systems. An automated process sends a signal to the process tools to shut down. The SEM tool is not required or production to classify the defects which leads to a greater Mean Time to Detect (MTTD). This process saves hours, costs and less processing time for a bad tool.

1)

Capability of 24x7 recipe build

2)

Eliminates product going on hold for no recipe.

3) Improves cycle time and hence addresses some queue time issues on some layers.

4) 5) build.

Frees up tool user time for other responsibilities.

Fewer tool down time in manufacturing for recipe

6) Recipe parameters are set up on an offline station and by one person so accuracy has improved for recipe writing.

7) Provides the capability to create multiple recipes for different flavors of layers during process development.

V.

PROCEDURE TO BUILD AUTO DERIVATIVE

Auto derivative recipe consists of two parts, namely on tool work and auto recipe run.

On tool Work comprises of creating a base recipe or a master recipe for every new device. Best known modes study need to be done for multiple layers to understand the sensitivity parameters and optics modes for inspection. With the knowledge of inspection modes, multiple recipes could be created offline without wafer on the tool.

Figure 4: IBM methodology for automated tool monitoring

During the auto recipe run the tool will relearn alignment sites and light level for inspection optics and perform an inspection scan and send the data. In the event of higher

nuisance rate on the recipe, offline tweak on the recipe is possible without having wafer on the tool.

Figure 6: Concept of auto derivative recipe As illustrated in Fig 6 Auto Derivative Recipe feature provides a way for the user to setup recipes for different process levels (Process level X, Y , Z) without the actual wafers using one base recipe (Process level A) and have the wafers from levels X, Y and Z to come on the inspection tool get trained automatically and run automatically without any user intervention.

Figure 7: Auto Derivative recipe flagged DOI detection across three different lot flavors

VI.

CONCLUSION

At IBM We have demonstrated that this concept works well on multiple 32 nm wafers as a proof of concept and planning to use this on multiple 22 nm and 32 nm levels and provide a way to automatically quantify any small process changes in early development.

Implementation of real time iDO classification results in less SEM time and greater accuracy in classifying defects than production personal. As a result accurate iDO faster Feedback from inspection lots to shutdown tools results in direct cost savings to the factory.

Figure 6 shows that when auto derivative recipe was scanned across three different lots, inspection tool was able to capture all the expected DOI population with non visual defects lower than 12%.

Auto derivative recipe creates quicker recipes with pre existing information on similar process levels. With minimum to no tweaks on auto derivative recipes multiple recipes could be created for different process flavors which help to get faster MTTD in developing 32 and 22nm process technologies.

VII. ACKNOWLEDGEMENT: IBM would like to recognize Robert Teagle, retired IBM, for his contribution to this work