Semiconductor Tool Condition Monitoring Using Contributing Data
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Solution Overview
Problem
Existing maintenance scheduling for processing tools relies on fixed time intervals, which can lead to inefficient and inaccurate maintenance activities as it does not account for the actual usage and condition of the tools, resulting in potential over- or under-maintenance.
Innovation Solution
A monitoring apparatus that segregates processing characteristic data into contributing and non-contributing periods, using techniques like threshold comparison, pattern matching, and Gaussian mixture analysis to determine the tool's condition, and employs fault logic to predict maintenance needs by combining and extrapolating contributing data using mathematical models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed time interval maintenance scheduling is used, then maintenance activities are performed regularly, but maintenance efficiency deteriorates due to over- or under-maintenance
Solution Approach 1:
The patent transitions from static fixed-time maintenance scheduling to dynamic condition-based maintenance scheduling. The system continuously monitors processing characteristic data and dynamically adjusts maintenance timing based on actual tool condition and usage patterns, ensuring maintenance is performed neither too early nor too late.
Solution Approach 2:
The patent changes the parameter basis for maintenance scheduling from fixed time intervals to variable parameters including processing characteristic data, tool condition indicators, and usage intensity. This allows maintenance timing to adapt to actual operational conditions rather than following a rigid schedule.
2Quantity of substance
If all processing characteristic data is used for condition assessment, then data completeness is improved, but measurement precision deteriorates due to inclusion of non-contributing data
Solution Approach 1:
The patent segments processing characteristic data into contributing and non-contributing periods based on tool operational states. By dividing the data stream into relevant and irrelevant segments, the system maintains data completeness while improving condition assessment accuracy by focusing analysis only on contributing data.
Solution Approach 2:
The patent extracts and isolates contributing processing characteristic data from the total data set by identifying and removing non-contributing periods. This extraction process ensures that condition assessment uses only relevant data, improving precision without losing overall data completeness for other purposes.
Data Source
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AI summary
A monitoring apparatus for monitoring at least one processing tool, a method and computer program product are disclosed. The monitoring apparatus for monitoring at least one processing tool during processing of an effluent stream from a semiconductor processing tool, comprises: reception logic operable to receive processing characteristic data generated during the processing of the effluent stream; segregation logic operable to segregate the processing characteristic data into contributing processing characteristic data associated with contributing periods which contribute to a condition of the at least one processing tool and non-contributing processing characteristic data associated with non-contributing periods which fail to contribute to the condition; and fault logic operable to utilise the contributing processing characteristic data and to exclude the non-contributing processing characteristic data when determining a status of the condition. In this way, only data generated during periods which contribute to the condition of the tool is used when determining a status of the condition of that tool. Other data generated during periods which do not contribute to that condition are ignored or omitted so that an accurate assessment of the status of the condition of the tool can be determined. This helps to provide an accurate and more reliable assessment of the status of the condition of the tool.