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Multi step & product with step dependence
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All process and inspection steps can be modeled for multiple products. An inspection tool can sample multiple steps/products. Multiple inspection tools can also sample the same process step. Dependent process steps are not treated independently.
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Process tool model
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If desired, all process tools are modeled explicitly. This makes IP1.0 a regular fab capacity model as well. All tool downtimes are part of queueing calculations.
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Multiple defect & excursion types
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Each tool cluster and/or process step can have multiple defect types. Each one with its own excursion characteristics. Excursions can impact one or multiple steps/products.
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Preventive maintenance (PM) impact
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Impact of PMs on tool availability and excursion yield losses is captured.
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Simultaneous excursions
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A process tool cluster can have more than one excursion taking place simultaneously.
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MTBF/MTTR impact
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Non-excursions related MTBF/MTTR of all tools is factored into queueing calculations.
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Defect capture rates
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Inspection tools have defect specific capture rates (also known as defect detection probabilities).
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Fab cycle time impact
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A dynamic queueing model captures the cycle-time impact of capacity and sampling plans.
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Wafer sampling
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Wafer sampling can be specified as % of lots, % of wafers, and % of wafer area to inspect.
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Fab throughput
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Fab throughput is a function of capacity and sampling plans.
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Review sampling & classification
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Excursion detection impact of review percentage and classification accuracy & purity is taken into account.
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Excursion signal propagation
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Excursion signals are seen at probe and can propagate to downstream inspection steps.
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Excursion signal to noise & yield impact.
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Excursion types have their own signal to noise ratio and yield impact inputs. The data analysis module can provide these inputs.
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False alarm response, root cause analysis, & excursion fix
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These events & their durations impact fab costs, yield, and process tool availability.
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Lot-to-lot & wafer-to-wafer variance
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These variances impact how many wafers it is necessary to sample per lot.
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Simulation verification
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Analytic approximations used are verified with simulation models for accuracy.
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Material handling model
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Model has inputs for the travel time lots experience on the material handling system.
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Optimization capability
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A genetic algorithm is used to optimize capacity and sampling plans.
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