Autonomous Learning System for Semiconductor Manufacturing Data Analysis
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Solution Overview
Problem
Current automated equipment lacks autonomy in analyzing data generated during complex processes, failing to recognize emergent phenomena that could optimize performance, as data processing remains non-autonomous and static, missing rich relationships among disparate data points.
Innovation Solution
An autonomous biologically based learning tool system that employs semantic networks with a memory platform, processing platform, and knowledge communication network, inspired by human brain structures, to analyze data and improve tool performance through learning and adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If non-autonomous static programmatic data processing is used, then data can be operated upon with existing computing resources, but the data fails to drive the analysis process itself and much of the rich relationships among data can be unnoticed
Solution Approach 1:
The patent implements self-service through autonomous agents that automatically analyze manufacturing data without human intervention. These agents autonomously navigate the data landscape, identify relationships, and generate insights, allowing the system to serve itself in the data analysis process rather than requiring external human operators.
Solution Approach 2:
The system employs dynamic adaptive agents that can modify their analysis approaches in real-time based on the data they encounter. These agents dynamically adjust their search strategies, prioritize different data relationships, and evolve their analysis methods, transforming static programmatic processing into a dynamic, self-adapting system.
2Adaptability or versatility
If specific analysis is designed and focused on a specific type of relationship, then that specific relationship can be detected, but emergent phenomena that can originate from multiple correlations among disparate data remain unnoticed
Solution Approach 1:
The patent implements multi-functional autonomous agents capable of performing diverse analysis tasks across different data types and relationship kinds. Rather than creating separate specialized analyses for each relationship type, universal agents can adapt their functionality to detect various relationships including emergent phenomena, allowing a single system to handle multiple analytical functions.
Solution Approach 2:
The system adds a new dimension to data analysis by introducing autonomous agents that operate in an agent-based computational space rather than traditional programmatic execution. This dimensional shift enables the system to explore data relationships in novel ways, discovering emergent phenomena that traditional single-dimension analysis methods would miss.
3Ease of operation
If human intervention is used for examination and interpretation of collected information, then data can be understood in context, but the processing of collected data remains a non-autonomous enterprise
Solution Approach 1:
The patent replaces the mechanical system of human data interpretation with autonomous software agents that perform analysis functions. These agents substitute human cognitive processes with automated computational methods, maintaining the interpretive capability while eliminating the need for human intervention in the actual data processing and analysis tasks.
Data Source
AI summary
An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. The network receives data from the tool system and from an external actor through the interaction manager. Both the memory platform and the processing platform include functional components and memories that can be defined recursively. Similarly, the one or more tools can be deployed recursively, in a bottom-up manner in which an individual autonomous tools is assembled in conjunction with other (disparate or alike) autonomous tools to form an autonomous group tool, which in turn can be assembled with other group tools to form a conglomerated autonomous tool system. Knowledge generated and accumulated in the autonomous learning system(s) associated with individual, group and conglomerated tools can be cast into semantic networks that can be employed for learning and driving tool goals based on context.