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Improving Manufacturing Process Efficiency Through Automated Analysis

by Laurie Union, Product Marketing Manager

Cycle time and throughput are major cost issues in semiconductor manufacturing today. Yield and process engineers spend substantial time reacting to defective lots on a one-to-one basis, thereby reducing the total impact they can have on the yield-improvement process. Installation of KLA-Tencor’s new automated analysis system together with the company’s on-line automatic defect classification (ADC) system at a customer site, has been shown to greatly improve cycle time and reduce manufacturing costs in semiconductor processing.

Two of KLA-Tencor’s yield management solutions were used for the purpose of this study — Klarity™ and IMPACT/Online™ ADC. Klarity, KLA-Tencor’s automated analysis system, embeds expert decisionmaking processes in the company’s defect analysis software resulting in accelerated resolution of problems impacting fab yields. IMPACT/Online ADC, the company’s on-line automatic defect classification system, quickly and accurately classifies defects in real time according to userdefined categories, surpassing the capabilities of manual classification techniques.

Engineers at a major semiconductor manufacturing site in the United States were assigned the task of implementing a more automated system to transfer some of the 22

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routine analyses to IMPACT/Online ADC and Klarity.

IMPACT/Online ADC was implemented to eliminate the need for time-consuming manual classification.

Klarity was implemented to automate complex manual analyses and, thereby, reduce lot disposition overhead.

Image classifications were taught to the IMPACT/ Online ADC system. This was completed prior to the implementation of Klarity to allow the system to make intelligent classifications and thus move some of the routine lot dispositioning tasks from the hands of the engineers and technicians to the fab operators. Klarity was installed and connected to all defect measurement systems including the IMPACT/Online ADC system.

Klarity, with its unique Decision Flow Analysis™ (DFA) capability (figure 1), allowed the engineering team at this site to build recipes that encapsulated the kinds of decisions and process instructions they would normally perform on a lot-by-lot basis. Klarity recipes were tested on two of their high-volume semiconductor products with more than 15 zones (process sectors). One of the recipes incorporated statistical process control where lots were tracked at each process zone with specific limits and then dispositioned automatically (figure 2). This was done as the data was being processed across the network and was loaded into the database in real time.

improvement has been approximately 300 hours per month. Engineers are now spending more valuable time investigating the “non-conforming” defects that do not show up routinely.

Figure 1. Klarity’s DFA recipe, which captures complex engi-

IMPACT/Online ADC coupled with Klarity has added value to the manufacturing process by increasing this team’s productivity and time-to-information by automating their process so that more time is spent on yield learning than on analyzing lots that go outside the control limits. The user study shows that cost savings are dramatic, operator efficiency increases twofold and engineering and technician productivity goes up by approximately 20 to 50 percent since time is now spent solving yield problems.

neering analysis methodologies into simple flowchar ts, was used to accelerate customer’s time-to-information.

With Klarity, after a lot has been inspected and classified using IMPACT/Online ADC, the data is loaded into the defect database. This event triggers an analysis recipe which is pre-set using Klarity’s Scheduler.

Automatic execution of this recipe will determine if a lot is in or out of control. If the lot is out of control, Klarity’s built-in use of conditionals and filters allows it to make defect type-based decisions in order to disposition the lot. Through Klarity’s capability to merge individual defect classifications into distinct groups, the team was able to further segregate the ADC-classified defects into “killer” and “non-killer” sub-groups. This simplified their analysis process and helped them filter data for subsequent analysis steps. The “killer” defects were further analyzed automatically through Klarity’s DFA. The process steps contributing to the killer defects were identified, and any new defect types that were not grouped by Klarity as killer or non-killer were automatically sent to off-line review and SEM (scanning electron microscope) systems for further investigation.

Since the implementation of Klarity and IMPACT/ Online ADC, this team has experienced dramatic reductions in cycle time. The actual cycle time

Figure 3. Stacked bar chart shows Klarity’s segregation of the ADC-classified defects into “killer” or “non-killer” groups. Once this chart is created, an automatic filter can be assigned to output only the “killer” defects to a KLA-Tencor results file for SEM review.

For more information call 800.450.5308 circle RS#014

KLA-Tencor Summer ’98 Trade Show Calendar July 13-17

SEMICON West, San Francisco & San Jose, CA, USA July 13-15 Wafer Processing, San Francisco, CA, USA July 15-17 Test, Assembly & Packaging, San Jose, CA, USA July 24-25 HeadMedia Penang, Penang, Malaysia July 27-30 Datastore Asia ’98, Singapore September 16-18 BACUS Photomask Tech & Mgmt.

Redwood City, CA, USA September 22-24 Diskcon San Jose, CA, USA Figure 2. Klarity’s SPC chart was scheduled by the customer to automatically identify excursions.

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