Proxy Interpreter for Legacy System Automation
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Solution Overview
Problem
Legacy manufacturing systems in automated environments, such as semiconductor facilities, require significant effort and expense to migrate or upgrade due to human error and the complexity of their architecture, leading to inefficiencies and increased production costs, especially in setup, configuration, and quality inspection processes.
Innovation Solution
A Proxy Interpreter system that uses Reinforcement learning to monitor and replicate human operator actions, eliminating the need for human intervention by capturing and analyzing keyboard and mouse inputs, and applying Deep learning for defect inspection, allowing for automated operation of legacy equipment without software installation on the legacy system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If legacy manufacturing systems are migrated or rewritten, then system efficiency and functionality are improved, but cost and time expenditure increase significantly
Solution Approach 1:
The patent introduces a proxy interpreter as an intermediary layer between the user and the legacy system. This mediator captures and translates user interactions into automated commands, enabling modern automation capabilities without directly modifying or replacing the legacy system architecture, thus avoiding time-consuming migration while improving productivity
2Adaptability or versatility
If manual operations are performed by human operators, then system flexibility is maintained, but human error and fatigue reduce quality consistency
Solution Approach 1:
The proxy interpreter enables the system to serve itself by automatically capturing, analyzing, and executing commands without human intervention. The system learns from recorded operator actions and autonomously performs setup, configuration, and inspection tasks, maintaining flexibility while eliminating human error and fatigue-induced inconsistencies
Solution Approach 2:
The patent implements feedback mechanisms where the proxy interpreter continuously monitors system responses and adjusts its command execution accordingly. This closed-loop approach ensures that automated operations maintain the adaptability of manual operations while achieving consistent quality through systematic feedback-driven corrections
3Adaptability or versatility
If legacy systems are upgraded to new equipment, then new inspection features are obtained, but capital spending and production costs increase
Solution Approach 1:
The proxy interpreter serves multiple functions including command capture, translation, automation execution, and system learning within a single software layer. This universal intermediary enables legacy systems to perform modern inspection and automation tasks without requiring expensive hardware upgrades, thereby maintaining versatility while controlling costs
4Extent of automation
If software is installed on legacy systems to automate operations, then automation capability is improved, but system complexity and compatibility issues increase
Solution Approach 1:
The patent segments the automation functionality into a separate proxy interpreter layer that operates independently from the legacy system core. This segmentation allows automation capabilities to be added without increasing the complexity of the legacy system itself, as the automation logic resides in the external interpreter layer that communicates through standardized interfaces
Data Source
AI summary
The present disclosure generally relates to upgrading existing automated legacy systems. More specifically, the present disclosure relates to system and method for a proxy interpreter system to collect and consolidate the setup, configuration, operation and quality inspection data from a plurality of interfacing devices and controllers of legacy systems and subsequently build a Reinforcement learning module using the consolidated data to perform all the functions automatically without the intervention of a human operator. The consolidated data in the proxy interpreter module may be further analysed using Deep learning methods for data analytics and artificial intelligence to reliably and consistently classify the defect criteria of products to further enhance the quality of the inspection. The defect criteria classification enables the Proxy interpreter system to highlight potential problems and aid in preventive maintenance of the legacy automated systems. The Proxy interpreter system enables legacy systems to adapt and scale to manufacture newer products with no human intervention whether it is related to operation of the legacy equipment or in the process of quality control.