Analysis

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Intelligent Line Monitoring

Maximum productivity through an integrated and automated strategy

by Tom Pilon, IBM Microelectronics Division; Mark Burns, Verlyn Fischer, Matthew Saunders, KLA-Tencor

The industry’s need for greater volumes ofintelligently collected defect data is mirrored at the state-of-the-art 0.25 µmtechnology fabrication facility of IBM’sMicroelectronics Division, which producesmultiple memory and logic devices across anumber of technologies. IBM recognized theneed to have a system that would help solveyield problems at a reasonable cost, maximizefab productivity and offer the flexibility tomake enhancements with the advances intechnology and manufacturing capacity.KLA-Tencor’s Intelligent Line MonitoringSystem (ILM) was installed to assess theeffectiveness of such an integrated approachto yield management.

What is an intelligent line monitor?ILM is an integrated set of defect inspectionsystems, automatic defect classification(ADC) systems, optical review tools, scanningelectron microscope (SEM) defect reviewtools, and a defect database and analysis system (figure 1). An intelligent line monitoris used to monitor and diagnose processexcursions, provide information necessary to

Maximizing the number of yielding parts per wafer while minimizing the cost to produce each part is the goal of any semi-conductor fabricator. For this reason, considerable investment is placed on ramping yields and protecting them once theymature. The task for the semiconductor industry becomes more challenging as critical dimensions decrease, the number ofprocess steps and their interdependence increase, and as throughput becomes an ever-demanding factor. The result is that asthese changes occur in the production environment, yield engineers require larger volumes of intelligently collected data.They also require the tools to adequately process that data and make responsive changes on the production line to ramp andprotect yield.

ramp yields on new products or technologies, and provide information necessary to predict yields.

As a product flows through the manufacturing line,samples of wafers are pulled, fed into the ILM system,and returned to the production line. As the producttravels through the ILM system, wafers are inspected andreviewed. Data are exchanged between the various com-ponents in the ILM system.

The ILM solution implemented at IBM is comprised ofmultiple KLA-Tencor 2132/35 defect inspection sys-tems, each of which was equipped with IMPACT ADC

Figure 1. Intelligent line monitoring system flow.

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software, scanning electron defect review microscopeswith automatic defect location (ADL) capabilities such as the Amray 3800, off-line optical defect reviewstations such as the CRS-1010, 2552 data analysis stations, in-house operational systems, in-house analysissystems, and a Quest defect analysis system.

The fundamental difference between an integrateddefect reduction system and a non-integrated tool set isthat the integrated system leverages the capabilities ofthe point products through integration and automationto generate a maximally informative and cost-effectivesample (termed as “smart sample”). Such integrationhelps provide maximum information about only thosedefects that most detract from yield. This type of sampling strategy provides the greatest impact towardimproving chip yields and fabricator productivity.

Because SmartSampling™ is automated by the ILM system, data can be continuously collected as productsmove through the production line. SmartSampling provides information which allows a line monitoringsystem to optimally detect process excursions, predictyield, and assist in yield learning. It does this by providing the source, type, and quantity of defects forproducts and technologies at the various process levels.Distillation processing is the key feature of an ILM system used to produce a smart sample. It does this by choosing defects on which to collect additionalinformation based on the defects’ potential impact on yield and other current in-line information.

Improved productivityInstallation of the ILM system at IBM and its ability to intelligently sample the production line generatedseveral measurable productivity improvements:

Reallocation of ResourcesBy automating the optical review process, valuableresources can be reallocated from manual review activi-ties (such as performing review, training, or verification)to other value-added processing tasks or higher-levelyield improvement tasks.

Since the installation of ILM less than a year ago, IBMhas reallocated up to 40 percent of its review operatorsto other work. In addition, time spent on training andverifying classifiers has decreased with the implementa-tion of ADC versus manual classification techniques(figure 2). This occurred because ADC training sets arefixed, whereas human memory and judgment is subjec-tive and varies with each operator’s level of expertiseand knowledge of the defect source, defect-kill poten-tial, operator mood, and time during the week or shift.Furthermore, IBM has recently negated its originallyestablished need to increase their number of manualreview stations by 43 percent.

Cycle Time ReductionThe ILM solution reduced cycle time in three ways.First, by decreased defect review times. ADC has beenshown to require far less time to classify a defect thanmanual review (by as much as 66 percent). Second, bydecreased queue times. By coupling wafer inspectionand optical review, the queue time between these stepswas completely eliminated. Queue time is the timewafers sit on a shelf in between the inspection andreview steps and has been measured to be as much as 70minutes (on average) when performing manual review.

Using manual review versus ADC review systems on adefect sample set showed that the time savings with theADC system were significant (figure 3). Based on agate-oxide classifier, cycle time was reduced by as muchas 67 percent with in-line ADC versus off-line manualreview.

The third way cycle time was reduced was with the useof a minimized sample size. While reduction in thetime taken to process defects during review is animportant contribution to decreased cycle times, thegreatest benefit derived with the ILM solution is thatthe sample set can be smaller yet contain all the criticalinformation. This is important because a sample that istoo large and takes too long to measure can cost morein lost production than in lost yield. The ILM solutionallows on-the-fly defect filtering, which decreases theimpact of doing off-line optical and SEM review on

Figure 2. Reduction in time spent on classifier training and verification.

Figure 3. Cycle time for three 8" wafers, with 100 percent cover-

age, 0.62 µm pixel, and 100 defects classified per wafer.

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total cycle time. This is especially important sinceSEM defect review is a costly inspection.

Figure 4 shows how the number of defects that aresent for ADC on optical review tools such as the Confocal Review Station and SEM defect review toolssuch as the Amray is reduced by using smart samplingtechniques.

Increased FlexibilityModular design of the ILM system allows inspectionsystems to be swapped, feature extensions to be madeor a system to be conveniently expanded and enhancedas demanded by shifts in technology and capacityrequirements. The benefit of increased flexibility, whichis especially important in a manufacturing environ-ment, is improved and protected cycle time.Component similarity of tools in the ILM systemallows the user to run a product interchangeablythrough similar tools eliminating overheads associatedwith set-up and extensive recipe management for eachindividual tool.

For the inspection/ADC components in an ILM systemto be interchangeable they must match. Matchingrequires that defects be identified equally well on oneor more inspection tools and that defect classificationcalls be similar on one or more ADC review systems.In other words, a lot placed at any inspector/ADC sys-tem will generate the same wafer map and reviewpareto as it would at any other inspector/ADC system.

The addition of database components to IBM’s ILMsystem, and feature extensions that were made with theaddition of ADC as an option to the defect inspectordemonstrate the ILM system flexibility. Inspectors herehave been matched on three product levels (at twoinspection sensitivities each) to 95 percent while ADCsystems have matched in the neighborhood of 80 percent on product wafers and up to 97 percent on adefect standard wafer (figure 5). In addition, the meansto track and maintain ADC matching has been studied.

ADC classifier extendibility across products and similarprocess levels for a given technology has limited theset-up time and classifier maintenance duties to rangesacceptable in a manufacturing environment. For exam-ple, without classifier extendibility, a fab running twotechnologies, two products each, and three via levelsper product would require 12 classifiers. However, withclassifier extendibility, that same fab would need tobuild only two ADC classifiers. At IBM, classifierextendibility has been shown to reduce classifier cre-ation by 80 percent.

Yield Protection and EnhancementThe time to detect a yield-limiting process excursion isthe sum of the beta risk, inspection time, and reviewtime. The beta risk is the time that a process is out-of-control but undetected and depends on a number ofparameters, including production rates, line samplingstrategy, and defect count statistics.

Data gathered on products at the via levels were usedto calculate time-to-detection using manual defect clas-sification (MDC) and ADC techniques (figure 6). Thegreatest contribution to detection time was the betarisk. In addition, it has been shown that review accura-cy has a significant impact in reducing the beta riskcontribution. The model that was used to make thiscalculation was developed by the CompetitiveSemiconductor Manufacturing (CSM) AutomatedInspection Focus Study Research Group.

Detection delay may also be represented in terms ofrevenue loss per hour. The total cost associated with an

Figure 4. Cycle-time reduction based on smart sampling.

Figure 5. ADC matching performance.

Figure 6. Total detection delay for via-level excursion monitoring

strategies.

excursion is the sum of five components. Beta risk isthe lost revenue due to product failing because of anout-of-control situation. Inspection cost is associatedwith the cost to operate an inspection system. Reviewcost is associated with the cost to perform review. Inthis model, ADC review cost was rolled into the costof inspection. Source identification is a measure of thecost to isolate the cause of a measured excursion, andfixing cost is associated with the cost of resolving theyield-limiting problem.

F E A T U R E S

Figure 7. Revenue loss per hour for via-level excursion monitoring

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The two greatest contributions to the total revenue lossare the beta risk and cost of review (figure 7). Revenueloss due to excursions at the via levels has been reducedby 36 percent with the implementation of the ILMsolution.

The solutions for future challengesThe ILM system serves to improve productivity by limiting the number of wafers exposed to yield-limitingconditions, allowing valuable resources to be reallocatedto value-added processing tasks and reducing samplingcycle times while maintaining the integrity of the sample data.

As the ILM solution matures, new features will beadded which will further reduce time-to-results. Forexample adaptive sampling, singular integrated inter-faces, central inspection and classifier creation and management, intelligent classifier builds, signatureanalysis, automated engineering analysis and decisionmaking, parametric analysis, and ADC on SEM andlaser-based inspection tools will become standard features necessary to keep pace with the increasingdemands of the industry.

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