Statistical Design Closure Optimizing Chip Yield
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Solution Overview
Problem
Current chip design closure processes are complex and unpredictable, leading to inefficient resource allocation, prolonged project timelines, and increased costs due to over-constraining and reliance on rules of thumb or additional personnel.
Innovation Solution
A statistical design closure method that utilizes historical data to calculate resource targets and optimize resource allocation by focusing on high-impact tasks, minimizing over-constraining, and reducing margins, thereby achieving efficient resource use and faster turn-around-times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If margins are added or over-constraining is applied in early phases to account for unpredictability, then timing risks are handled, but resource efficiency decreases and turn-around time increases
Solution Approach 1:
The system performs preliminary statistical analysis of historical design closure data before the actual design closure process. By pre-calculating resource consumption patterns, yield trends, and closure trajectories from historical projects, the system establishes predictive models that guide resource allocation and constraint setting in advance, eliminating the need for excessive margins and over-constraining during execution
Solution Approach 2:
The system implements continuous monitoring and feedback mechanisms during design closure runs. By tracking real-time progress against statistically predicted trajectories and comparing actual resource consumption with historical patterns, the system dynamically adjusts constraints and resource allocation, replacing static over-constraining with adaptive, data-driven control that maintains reliability while improving efficiency
2Reliability
If multiple runs and multiple loops are used to reach design closure, then timing closure is achieved, but project timeline extends and resource consumption increases
Solution Approach 1:
The system pre-analyzes historical design closure data to establish predictive models of closure trajectories, resource consumption patterns, and yield trends before initiating the current design closure process. These preliminary statistical models enable the system to predict the number of iterations needed and optimize the closure path from the start, reducing unnecessary repeated runs and loops
Solution Approach 2:
The system dynamically adapts the design closure process by continuously monitoring progress and adjusting constraints, resource allocation, and closure strategies based on real-time feedback and historical patterns. This dynamic approach replaces static, rigid multi-loop processes with flexible, data-driven iteration that converges faster by learning from historical outcomes and adapting the closure path dynamically
3Productivity
If more licenses and machines are used for parallel operations, then design closure capacity increases, but resource cost increases
Solution Approach 1:
The system enables self-service optimization by automatically analyzing historical data, predicting resource requirements, and optimizing the allocation of licenses and machines without requiring manual intervention or over-provisioning. The statistical models self-adjust resource allocation based on actual project needs and historical patterns, achieving high utilization rates with fewer resources
Solution Approach 2:
The system changes the parameters of resource allocation by using statistical models to predict optimal license and machine usage patterns. Instead of allocating fixed large quantities of resources, the system dynamically adjusts resource parameters based on historical data analysis, project complexity, and predicted closure trajectories, achieving high productivity with optimized resource quantities
4Productivity
If statistical data from historical projects is used to calculate targets, then resource allocation is optimized, but data processing complexity increases
Solution Approach 1:
The system creates universal statistical models that serve multiple functions: analyzing historical data, predicting resource consumption, estimating yield trends, optimizing targets, and monitoring progress. This multi-functional approach consolidates what would otherwise require separate complex processing systems into a unified statistical framework that handles all these tasks through a common data processing architecture
Solution Approach 2:
The system creates simplified statistical representations (copies) of complex historical design closure data. By extracting key patterns, trends, and parameters from historical projects and storing them as statistical models, the system captures the essence of complex historical data in a compact form that can be efficiently processed and applied to predict and optimize current projects without handling the full complexity of raw historical datasets
Data Source
AI summary
A method of statistical design closure is disclosed. The method generally includes the steps of (A) reading statistical data from a database, the statistical data defining a plurality of chip yield improvements, one of the chip yield improvements in each one of a plurality of design closure categories respectively, the chip yield improvements capturing historically trends based on a plurality of previous projects, (B) calculating a plurality of targets of a current design closure project based on the statistical data, one of the targets in each one of the design closure categories respectively and (C) generating a resource report to a user that indicates a plurality of resources expected to be used the current design closure project.