Hierarchical Production Data Interface for Dense Manufacturing Analysis
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Solution Overview
Problem
Dense big data in semiconductor manufacturing and other contexts poses challenges due to its high density, making it difficult to summarize and analyze effectively, leading to information overload and the inability to connect relevant information across multiple summaries, especially when there are more measured variables than manufacturing samples.
Innovation Solution
An exploratory data interface is provided, enabling the display of parametric data through electronic spreadsheets and hierarchical topology-based analysis, allowing users to navigate complex data structures, highlight significant items, and create linkages between logistical and electrical data for efficient analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If dense big data is summarized at a high level, then catastrophic problems become visible, but only catastrophic problems are visible and detailed information is lost
Solution Approach 1:
The patent segments dense big data into hierarchical levels of summary, allowing users to navigate from high-level catastrophic problem identification to detailed parameter analysis. Each level of the data interface provides appropriate granularity, preventing information loss while managing complexity through structured division of data representation.
Solution Approach 2:
The patent adds a dimensional aspect to data presentation by creating multi-level hierarchical views. Instead of a single flat summary, the system provides multiple dimensions of data organization (summary levels, parameter categories, relationship types), enabling users to explore data at appropriate depths without being overwhelmed by complexity.
2Loss of information
If dense big data is summarized at a detailed level, then complete information is available, but information overload occurs
Solution Approach 1:
The system segments comprehensive data into manageable hierarchical levels, presenting complete information only when users navigate to appropriate detail levels. This segmentation allows information completeness to be maintained in the backend while the frontend presents only the necessary amount of detail for current analytical needs.
Solution Approach 2:
The data interface is dynamic, allowing users to expand or collapse detail levels based on their analytical needs. The system adapts the amount of information presented in real-time, transitioning from high-level summaries to detailed parameter views and back, thereby maintaining ease of operation while preserving access to complete information when required.
3Adaptability or versatility
If multiple data summaries are created, then different perspectives are available, but connecting information across summaries becomes difficult
Solution Approach 1:
The patent implements a universal data interface framework that handles multiple data summaries and perspectives through a common hierarchical structure. This multi-functional system can represent different data views (summary levels, parameter types, relationship categories) using the same underlying architecture, enabling easy connection of information across different summaries through standardized navigation and search mechanisms.
4Extent of automation
If expert systems are used to handle dense big data, then automated analysis is achieved, but they cannot be trained and validated without human insight into data
Solution Approach 1:
The patent creates an intermediary data interface system that bridges automated expert system analysis and human expert insight. This intermediary layer provides structured, navigable data representations that preserve the information needed for human validation while enabling automated processing. The hierarchical interface serves as a mediator, allowing automated systems to operate on structured data while human experts can inspect and validate findings at appropriate detail levels.
Data Source
AI summary
A method for production analysis includes: receiving production data at a processor from a plurality of tools spatially arranged within a manufacturing facility; creating a hierarchal topology of the data in the processor, wherein each level of the hierarchal topology is based on a different one of a plurality of static parameters that are selected from a list consisting of: a tool identifier, a batch identifier, and a spatial orientation; displaying, at a user interface implemented by the processor, a first analysis of a first level of the hierarchal topology, wherein the analysis contains parameters related to other levels of the hierarchal topology; receiving, via the user interface, a selection by a user of a first parameter displayed on the first analysis; and updating the user interface to display a second analysis of a second level of the hierarchal topology that is related to the first parameter.