Spring 1999 Yield Management Solutions112

The problem of optimally applying inspec-tion equipment in defect inspection is verycomplex and only partially addressed1. Theproblem involves numerous interrelatedvariables such as the process technology,defect mechanisms, the inspection equip-ment, fab logistics, processing parameters,and financial data. Making the problemeven more complex, the fab’s inspectionrequirements are not static, they continu-ously evolve throughout a fab’s operationalphases. During the process transfer andyield learning phases, inspection is focusedon understanding and improving baselinedefect densities, as opposed to focused onexcursion control during the mature, fullproduction phase. Defect mechanisms andtypes also evolve — from a higher contentof systematic, process integration relatedissues during early phases — to more random-related process tool events at themature, full production phase. Defect excur-sion types and frequencies, wafer starts, anddevice average selling prices are just a few of the drivers that evolve and affect the opti-mum inspection strategy for each phase.

Advanced statistical and stochastic modelshave been developed to estimate the opti-mal defect inspection capacity, allocation,and operation (sampling strategy) in fabs.Sample Planner™ is a software program

Evaluating Inspection Strategies UsingAdvanced Statistical Methods

by Raman K. Nurani, Ph.D., Meryl Stoller, and Dadi Gudmundsson, KLA-Tencor; J. George Shanthikumar, Ph.D., University of California at Berkeley

Increasing fab construction costs, shortening product life cycles and eroding market prices are realities for today’s integratedcircuit (IC) manufacturers. In this competitive environment, cost-effective operations are an important part of a successfulbusiness plan. High yields have to be reached faster and maintained at lower wafer processing cost levels than ever before.Towards this goal, optimal capacity of inspection equipment and its allocation across different process steps are impera-tive, whether it is defect or metrology oriented.

based upon these models that uses an unprecedentednumber of variables to create and optimize a fab-wideinspection strategy. This software program can also beused as a tool for KLA-Tencor’s engineering and devel-opment to determine the best inspection technologyand configurations for future process technologies.

The sample planning problemIt has become well accepted that defect inspection toolsplay an important role in a fab’s yield managementstrategy. While few manufacturers currently operatewithout some type of defect inspection, many IC manu-facturers tend to view inspection as non-value addedand are overly conservative when planning inspectioncapacity. It is here that the sample planning problemarises, i.e. what types of inspections to perform, whereto locate them in the process, and how frequently toperform the inspections. The optimum level of inspec-tion is reached through the trade-off between the costof inspection operations, both fixed and variable, andthe cost of yield loss due to undetected yield-limitingdefects and process excursions.

The main decision parameters are: type of inspections(test wafer, product, or in-situ inspections), placementof the inspections (which process steps/tools), inspec-tion frequency (percent lots to sample, number ofwafers per lot, area per wafer), inspection sensitivitysetting, which parameters to track and respond to (sta-tistical process control scheme), the fraction of defectsto review, and inspection capacity. All of these parame-

ConsultingF E A T U R E S

quantify mean and variance of defects during in andout of control states, the propagation of defects to sub-sequent process steps, the types of excursions and thefrequency of excursions. Combining this informationwith yield and financial data allows the financial lossper year from excursions to be quantified. The financialloss due to excursions can be decreased by samplingmore often. To determine the cost of sampling wequantify: equipment sensitivity to defects, inspectiontool throughput, inspection tool operation, cost ofownership, and queuing/transit times. A stochasticalgorithm uses this information along with the excur-sion, yield, and financial data to calculate the overallcost. By iterating through several operationally feasiblesample plans, the algorithm determines the most costeffective inspection strategy.

Sample Planner 2A software tool called Sample Planner 2 has beendeveloped based on the above methodology. SamplePlanner 2 allows an unprecedented number of criticalvariables to be involved in sample planning optimiza-tion. Besides addressing the decision variables of thesample planning problem mentioned earlier, it incor-porates IC manufacturing issues such as, re-entrantflow, rework decisions, and complete process line modeling (300+ steps). The categories of data used by the Sample Planner are five: fab information, inspec-

ters are interrelated and each one gives rise to a set ofvariables that need to be understood. Overall, theproblem is so complex that no comprehensive solutionmethodologies existed prior to our efforts. The SamplePlanner cost model provides the framework and toolsto analyze critical fab parameters to develop an opti-mal inspection strategy with reasonable effort.

The excursion control methodologyIn its simplest form, the cost model methodology isbased around a recurring in-and-out of control cycleoccurring at each step in the process, see figure 2. Acycle starts where each step in the process is assumedto have an in-control mode of operation which deliversa high yield. After a random length of time an excur-sion takes place, causing lower yields. At this pointthe inspection sampling strategy determines howquickly the excursion is caught and fixed, restartingthe in-and-out of control cycle. The goal is to mini-mize financial loss by catching the excursions quickly,i.e. minimizing the time between excursion start anddetection. However, this needs to be done only for areasonable inspection cost, which is the essence of theoptimization. To do that, modeling mathematicallyhow the process behaves and how the inspection tools“see” the process is the foundation.

The widespread use of a standard statistical processcontrol (SPC) scheme results in accumulation ofimportant data from the processes. We process thisdata using statistical models and hypothesis tests to

Spring 1999 Yield Management Solutions 13

F E A T U R E S

Figure 1. Important decision parameters in sample planning.

Figure 2. The in-control and out-of-control cycle.

Figure 3. Sample Planner 2 inputs and outputs.

InspectionLayers?

InspectionMethod?

InspectionTools?

Percent ofLots?

Wafers PerLot?

InspectionDelays?

DefectSize?

e.g., 8 steps e.g., producttest wafer

e.g., 2XXX AIT

e.g., 40% of lots

e.g., 5 wafersper lot

e.g., queuing

e.g., 0.3 µmsensitivity

> 0.8 µ

> 0.5 µ

> 0.3 µ

Product

Test Wafer

In-situ

2XXX

AIT/SP

# De

fect

s

Proc

ess

Flow

Defect Size

Cyclestarts

Last samplebefore excursion

Excursionoccurs

First sampleafter excursion

Excursiondetected

Sourceidentified

Sourceeliminated

In-Control Out-of-Control

Material at Riskβ-risk

Fab Information

• Process flow• Baseline and excursion yields• Process cycle time• Average selling price• Test wafer costs• Labor rates• Re-entrant flow and photo

loop rework data

Inspection Technologies

• Inspection tool types• Capture rates• In-situ/Test wafer monitor• Throughput and Q times

Baseline Information

• Mean and variances• “In control” pareto• Defect propagation

Excursion Information

• Frequency by level• Yield impact• Out of Control pareto

Inspection Strategy

• Inspection points• Defect classification plan• DSA On/Off• Sampling plan• Control charts and limits

Input

Optimized Sampling StrategyBased Upon

• Excursion yield loss/costs• Inspection costs• Test wafer monitor costs• Lots at risk• False verification man hours• Root cause analysis time

Output

Malaysia

Israel

Spain

France

Italy

Ireland

Scotland

UnitedKingdom

United States

ChinaTaiwan

Holland

Korea

Singapore

Japan

AustriaGermany

F E A T U R E S

tion technologies, inspection strategy, baseline defectinformation, and excursion characteristics. The primarydata of interest in these categories can be seen in figure 3. This data is entered into the SamplePlanner database through a user friendly graphicalinterface where the user can outline many differentoperational scenarios to analyze.

Currently, the Sample Planner 2 software is being utilized in two ways, 1) by KLA-Tencor’s yield management consultants to help customer fabs deter-

Figure 4. Example output from Sample Planner 2 analysis.

S I N G L E P O I N T O F CO N TA C TTechnical Support Assistance

Scheduled or Emergency ServiceStatus Inquiries

Parts Ordering/Inquiries

L I V E7x24 Placement of Service Requests

Escalation Capability

C A L L T R A C K I N GAll Service RequestsAll Escalation Events

C E N T R A L R E S O U R C E S Service Report Filing

Performance ReportingAuto Notification for

Escalated Events

CUSTOMER RE SPONSE CENTERS

USA

1 - 8 0 0 - 6 0 0 - 282 9

EUROPE

0 8 0 0 - 1 74 728 ( U K )

0 8 0 0 - 9 0 - 0 3 - 8 0 ( F R A N C E )

1 3 0 - 8 1 - 6 5 - 8 3 ( G E R M A N Y )

1 6 7 7 - 8 0 - 3 7 0 ( I TA LY )

JAPAN

0 4 5 - 9 8 5 - 7 5 0 0

W O R L D W I D E S U P P O R T O P E R A T I O N S ( W S O )

mine the optimum inspection strategy for all phases of afab’s life cycle (from new fab planning, through yieldramp and into full production), and 2) by internal KLA-Tencorproduct development group to help determine the technology and configurations to use when developinginspection tools for future IC manufacturing processes. An example of the output from Sample Planner 2 analysisperformed for a customer is shown in figure 4.

ConclusionThe advanced statistical methodology developed by KLA-Tencor has greatly expanded the field of inspectionstrategy optimization. With its most recent capability,Sample Planner 2 gives users the critical ability to deter-mine and update/ evolve the optimum inspection strategythrough each operational phase. This methodology is nowbeing adapted to additional inspection avenues, such asreticle inspection and CD metrology.

1. Nurani, Raman K., Akella, Ram, Strojwas, Andrzej J. “In-line Defect SamplingMethodology in Yield Management: An Integrated Framework”. IEEETransactions on Semiconductor Manufacturing, vol. 9, No. 4, November 1996.

Adjusted total cost (million $/year)Total inspection time (hours/week)

Excu

rsio

n Co

sts

Insp

ecti

on H

ours

per

Wee

k

30% product lots, 4 wfrs per lot Increase product lots to 50%, Increase product lots to 100%tool monitors average 1 per day reduce wafers to 2 reduce wafers to 1

Line Monitor Excursion Cost/Inspection Capacity Analysis

Inspection Sampling Strategy

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