Substrate Processing Failure Logging for Faster Root Cause Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current substrate processing apparatuses face challenges in rapidly identifying the cause of failures, leading to increased downtime due to insufficient failure information being provided to users, which hinders prompt resolution and productivity.
Innovation Solution
A substrate processing apparatus equipped with a memory to store recipes, apparatus information, and failure data, including first apparatus-related information before a failure and second apparatus-related information after the failure, allowing for rapid identification of failure causes through a controller that stores and updates this data for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If only apparatus data at the time of failure is stored, then data storage is simple, but failure cause identification is slow
Solution Approach 1:
The system performs preliminary actions by storing apparatus data not only at the moment of failure but also for a predetermined period before the failure occurs. This advance preparation of data ensures that when failure happens, comprehensive information is already available, enabling rapid cause identification without needing to collect additional data afterward.
Solution Approach 2:
The failure data storage is segmented into multiple time periods: data at the exact failure moment, data during a predetermined period before failure, and data during a predetermined period after failure. This segmentation allows the system to capture comprehensive temporal information while organizing it in a structured manner for efficient analysis.
2Measurement precision
If comprehensive failure data including before and after periods is stored, then failure analysis is improved, but data storage complexity increases
Solution Approach 1:
The data storage system dynamically adjusts the time periods for data collection based on the failure event. The controller automatically determines the start and end times of the before-period and after-period relative to the failure moment, making the data collection window adaptive rather than fixed. This dynamic approach ensures comprehensive coverage without requiring complex pre-configuration of time parameters.
3Measurement precision
If apparatus data is collected for extended periods before and after failure, then cause identification accuracy improves, but data processing time increases
Solution Approach 1:
The system applies partial action by collecting data for a predetermined period before and after failure rather than continuously storing all operational data indefinitely. This selective time-window approach captures sufficient information for accurate cause identification while avoiding the burden of processing excessive historical data, achieving a balance between accuracy and efficiency.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
According to the present disclosure, there is provided a technique capable of rapidly identifying a cause of an occurrence of a failure and supporting a recovery. There is provided a technique that includes: a memory storing recipes, apparatus information reported from within the substrate processing apparatus and failure information; a process vessel in which a substrate is processed based on a recipe; and a controller for: (i) storing failure data in the memory together with an occurrence time of the failure contained in the failure information when the failure occurs in the substrate processing apparatus, the failure data containing first apparatus-related information reported before the failure during a first period defined in advance and the recipe in the memory; and (ii) adding second apparatus-related information reported after the failure during a second period defined in advance to the failure data.