Microlithography Machine Diagnostic System Using Symptom-Cause Database
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Highly complex microlithography machines, such as mask inspection apparatuses, face challenges in quickly identifying the cause of malfunctions due to their numerous components, leading to prolonged downtimes and inefficiencies in maintenance and repair processes.
Innovation Solution
A method that creates a database to assign causes to combinations of symptoms, allowing for automatic identification and corrective actions, and provides step-by-step guidance to operators to locate faulty components, while also optimizing service processes and sequences based on user input and machine states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the machine complexity increases to achieve higher functionality, then the machine can perform more sophisticated microlithography tasks, but the difficulty of identifying malfunction causes increases due to the large number of components
Solution Approach 1:
The patent segments the complex machine into multiple functional modules or subsystems, each with its own set of components. When a malfunction occurs, the system divides the diagnostic process into smaller segments by analyzing symptoms specific to each module, making it easier to identify the root cause without being overwhelmed by the overall system complexity.
Solution Approach 2:
The patent introduces an intermediary diagnostic system or software layer that acts as a mediator between the machine components and the operators. This intermediary automatically collects data from numerous components, correlates symptoms, and presents simplified diagnostic information to operators, reducing the difficulty of cause identification despite high machine complexity.
2Adaptability or versatility
If the number of machine components increases, then the machine becomes more capable, but the time required to identify and retrieve faulty components increases
Solution Approach 1:
The patent implements preliminary actions by pre-mapping the locations of all machine components and pre-establishing diagnostic criteria for each component. When a malfunction occurs, the system can immediately retrieve the faulty component's location and retrieval instructions without time-consuming searches, significantly reducing downtime while maintaining high machine capability.
Solution Approach 2:
The patent establishes a feedback system that continuously monitors machine components and provides real-time information about their status. When a component fails, the system immediately feeds back diagnostic information including the component's identity and location, enabling rapid response and minimizing downtime while the machine maintains its sophisticated capabilities.
3Reliability
If highly qualified staff are used to handle complex machines, then better expertise is available, but the assumption that staff can be experts for all components becomes less valid with increasing complexity
Solution Approach 1:
The patent implements a self-service diagnostic system that automatically guides operators through the troubleshooting process. The system provides step-by-step instructions, retrieves relevant component information, and suggests appropriate actions, enabling operators with varying expertise levels to effectively diagnose and resolve issues without needing to be specialists in all machine components.
Solution Approach 2:
The patent replaces the reliance on human expert knowledge (mechanical system of human cognition and experience) with an automated diagnostic system (electronic/information system). This substitution allows the machine to provide expert-level diagnostic capabilities regardless of the operator's specific expertise, effectively decoupling system complexity from the knowledge requirements of individual operators.
4Ease of operation
If operators must decide on service process sequences manually, then flexibility is maintained, but the time required to determine optimal maintenance sequences increases significantly
Solution Approach 1:
The patent establishes a feedback system that continuously monitors machine operation and component status, providing real-time information about which service processes are needed and in what sequence. This automated feedback eliminates time-consuming manual planning while maintaining operational flexibility by adapting service sequences based on actual machine conditions and priorities.
Data Source
AI summary
The invention relates to a method for operating a machine for microlithography which has a multiplicity of machine components. According to one aspect, malfunctions of these machine components that occur during the operation of the machine are each describable by a symptom, wherein the method includes the following steps: creating a database in which a cause is in each case assigned to different combinations of these symptoms, automatically recording the symptoms occurring within a predetermined time interval when a problem occurs during the operation of the machine and automatically assigning a cause to the problem on the basis of the recorded symptoms and the database.