Automated Megacell Generation for Critical Path Optimization
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Solution Overview
Problem
Current semiconductor IC fabrication processes are labor-intensive and time-consuming when optimizing electronic designs for performance requirements, particularly in optimizing standard cell combinations to improve speed or power consumption, as they often require manual customization and are specific to each IC manufacturer and technology node.
Innovation Solution
The method involves creating and using megacells, which are system-generated cells representing logic functions that can replace critical standard cell combinations to optimize performance, by generating a timing report, determining critical transistor counts, and creating a violation-free layout using a megacell netlist, allowing for automated optimization of critical paths without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual customization of standard cell combinations is used to optimize performance, then design optimization can be performed, but labor and time requirements increase significantly
Solution Approach 1:
The system performs automated optimization by generating timing reports, identifying critical paths, and creating megacell netlists without requiring manual intervention. The EDA tool automatically analyzes the design, determines optimal cell combinations, and produces violation-free layouts, allowing the system to optimize itself rather than requiring human expertise for each optimization step.
Solution Approach 2:
The patent replaces manual mechanical customization processes with automated computer-based optimization. Instead of manually selecting and customizing standard cell combinations, the system uses automated algorithms to generate timing reports, identify critical paths, and create optimized netlists, substituting human manual work with computational automation.
2Productivity
If automated optimization tools are used, then time consumption is reduced, but the ability to handle complex customizations may be limited
Solution Approach 1:
The system handles complexity by changing parameters at different levels of abstraction. It generates timing reports with various optimization parameters, identifies critical paths based on different constraints, and creates megacell netlists with adjusted parameters. This allows the automated tool to adapt to different optimization scenarios by modifying parameters rather than requiring manual reconfiguration.
Solution Approach 2:
The optimization process is segmented into distinct automated stages: timing report generation, critical path identification, megacell netlist creation, and violation-free layout generation. Each stage handles a specific aspect of optimization independently, allowing the system to manage complexity through modular automation while maintaining versatility across different design scenarios.
3Ease of manufacture
If standard cell library is used, then design implementation is simplified, but performance optimization is limited
Solution Approach 1:
The patent merges standard cells into megacells by identifying critical paths and generating combined netlists that represent multiple standard cells as a single functional unit. This merging allows the system to maintain the simplicity of standard cell libraries while achieving performance optimization through automated megacell creation, combining the benefits of both approaches.
Solution Approach 2:
The megacell netlist acts as an intermediary between the standard cell library and the final optimized design. It translates standard cell combinations into optimized structures while maintaining compatibility with manufacturing processes, serving as a bridge that preserves ease of manufacture while enabling performance optimization.
Data Source
AI summary
A method and system optimizes or improves an electronic design by analyzing various signal paths in the electronic design and selecting certain critical paths, for example, failed-timing paths, to optimize. The optimizing method extracts the cascaded logic gates to create a megacell representing the function of the critical path, compare test parameters of the megacell with the critical path, and incorporate the megacell into the electronic design if the test parameters improve by an optimizing constraint.