Magazine summer00 enhancing sensitvitity

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Enhancing Sensitivity and Throughput in Brightfield Inspection

by P.H. Wu, J.P. Wu,TSMC Fab 3; J. Liao, C. Chuang, K. Nafisi, M. Dishner, KLA-Tencor Corporation

This paper was presented at KLA-Tencor’s Yield Management Solutions Seminar during SEMICON/Europa in April 2000.

It was edited for this publication by Mark Keefer, KLA-Tencor Corporation.

As integrated circuit feature sizes continue to shrink, and device cycle times are reduced, defect inspection technology must continue to improve for device manufacturers to manage their yield. The introduction of the KLA-Tencor 2139 increased sensitivity, defect capture and throughput on its 2100-series brightfield optical imaging inspector product line. Primary sensitivity improvements are achieved by decreasing the pixel size and by automating the recipe development of segmented thresholding routines (AutoSAT). Productivity is enhanced by job queuing and faster edge die inspection (double detection of edge die defects without re-swathing).

From December 1999 to March 2000, TSMC’s Fab 3 facility evaluated a 2139 beta system versus the baseline 2138 system for throughput and sensitivity. All layers were 0.18 µm to 0.22 µm design rule logic devices. A 2138 system already installed in the fab was upgraded to a 2139 system.

Negligible baseline shift between the tools using the same inspection recipe and wafer was observed. Data were collected to show the robustness of job queuing, the sensitivity of the 0.16 µm pixel, the throughput using the new version 5.2 software, and the robustness of SAT recipes for production.

Additionally, the sensitivity improvement obtained by optimized SAT recipes (using AutoSAT software) relative to the baseline mean-range image processing method was evaluated.

Throughput results

After verifying that there was no baseline shift on Metal 4, Spacer, and Polysilicon 1 etch levels, inspection equipment throughput and productivity was tested. Inspection

equipment productivity was increased in two ways.

First, the multi-tasking feature of Windows NT software (KLA-Tencor 213x version 5.2) allows job queuing. While an inspection is in progress, the next inspection lot and recipe can be prepared, reducing idle time on the inspector. The time savings can be quite significant, especially in a SMIF fab with pod load and unload times. Figure 1 shows the time savings achieved using the job queuing feature for inspection of two lots (in the left and right cassettes), two wafers per lot.

Another throughput improvement in the 2139 is fast edge die inspection, referred to as the Mass Memory Edge Die (MMED) feature. Prior to MMED, double detection of edge die defects required that the edge die be re-swathed, which adds considerable time to the inspection (referred to as the TEO method, meaning “triple edge only”. First the wafer center is scanned, then the left side, then the right side). The MMED upgrade allows sufficient inspection data to be stored in the memory buffers so that the edge die do not require reswathing. Figure 2 compares inspection time on 0.22 µm Metal 3 etch wafers from five different lots (using 0.39 µm pixel). The average inspection time using TEO is 14 minutes 47 seconds; the average time using MMED is 12 minutes 18 seconds, a 17 percent improvement.

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2139 NT = 39 m 5.2 NT time saving: 10 m 54 s (>20%) Figure 1. Breakdown of time savings achieved using the job queuing feature.

Sensitivity results

Smaller pixel sizes can increase defect detection sensitivity for process levels that are limited by inspection resolution. The 2139 0.16 µm pixel was compared to the 2138 0.25 µm pixel. The increased capture of the 0.16 µm pixel on a 0.18 µm Polysilicon 1 etch level is shown in Figure 3. The defect counts are cumulative totals of five wafers from five different lots.

Figure 4 shows SEM images of some of the defect types detected using the 0.16 µm pixel in die-to-die (random) mode on the 0.18 µm design rule Polysilicon 1 etch level. These defects were not caught using the 0.25 µm pixel. In conjunction with the smaller pixel, a higherresolution camera provides sharper images for defect

review, making verification of real and false or nuisance defects during inspection recipe development easier.

Inspection sensitivity is determined by resolution (small pixel size) as well as suppression of pattern and process variation noise. Segmented Auto Thresholding (SAT) is an image processing technology used to suppress pattern and process noise. SAT segments the wafer image based on the gray level signature of the pattern and dynamically sets separate thresholds for each segment, resulting in higher sensitivity than the mean-range image processing method. An AutoSAT routine has been developed to simplify SAT recipe 1000 0.25µm pixel (2138)

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Figure 2. Comparison of inspection times for Metal 3 etch level.

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Figure 3. Poly etch defect capture comparison (0.16 µm and 0.25 µm pixels).

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Figure 6. SEM images of 0.22 µm design rule Metal 3 etch defect Figure 4. SEM images of 0.18 µm design rule Polysilicon 1 etch

types uniquely caught by the AutoSAT recipe

defects detected using 0.16 µm pixel.

setup. AutoSAT recommends optimal segmentation schemes from a pre-defined selection of templates, then automatically optimizes the threshold for each scheme.

The automation reduces recipe development time, and results in more consistent recipes. Figure 5 compares defect detection sensitivity of AutoSAT and mean-range image processing on a 0.22 µm logic device Metal 3 etch level. The defect counts are cumulative totals of nine wafers from nine different lots. Defect capture of particles in dense arrays and defocus defects in particular were improved.

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Using AutoSAT, the 2139 also uniquely detected several defect types not detected by mean-range image processing on this Metal 3 etch level.

Summary

The productivity and sensitivity improvements of the 2139 defect inspection system were evaluated. The job queuing feature improved tool utilization by over 20 percent. Inspection time per wafer using the 0.39 µm pixel was reduced by about 17 percent using the fast edge die mode. The smaller 0.16 µm pixel size increased defect capture relative to the 0.25 µm pixel, and additional sensitivity was achieved using the AutoSAT image processing algorithms. Additional data also showed that SAT reduced the level of nuisance defects such as blister defects on Metal 3 and 4 etch levels.

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Figure 5. Comparison of AutoSAT and mean-range image processing methods on metal etch.

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