Semiconductor Circuit Design System Cell Clustering Optimization
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Solution Overview
Problem
The increasing complexity of semiconductor circuits due to the growing number of cells and connections necessitates a methodology for optimizing cell placement to minimize area and wiring length, while improving performance, which existing EDA tools fail to achieve effectively.
Innovation Solution
A system and method that utilize a CPU and working memory to cluster cells based on their connection relationships, generating directed graphs to identify strong relations between cells, and placing them adjacent to each other to reduce wiring length and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of cells and nets in a semiconductor circuit increases to meet various functional requirements, then the functionality and versatility of the circuit is improved, but the complexity of placement optimization deteriorates and existing EDA tools fail to achieve effective optimization
Solution Approach 1:
The patent segments the complex placement optimization problem into two distinct phases: clustering phase and placement phase. The clustering phase groups cells into clusters based on net connections, while the placement phase optimizes the position of each cluster and cells within clusters. This segmentation allows the system to handle large-scale circuits by breaking down the overwhelming complexity into manageable sub-problems that can be solved sequentially.
Solution Approach 2:
The patent performs preliminary clustering of cells into clusters before the actual placement optimization. This preliminary action groups related cells together based on their net connections, creating a hierarchical structure that simplifies subsequent placement decisions. By pre-organizing cells into clusters with strong interconnections, the system reduces the search space for placement optimization and improves overall placement quality in large-scale circuits.
2Ease of operation
If automatic placement algorithms are used to handle numerous cells and nets, then the ease of operation is improved, but the manufacturing precision of placement deteriorates
Solution Approach 1:
The patent implements a dynamic two-stage placement approach where the first stage (clustering) dynamically groups cells based on net connection analysis, and the second stage (placement) dynamically optimizes positions within each cluster. This dynamic adaptation allows the algorithm to adjust its strategy based on the specific circuit topology, achieving both automation and high placement precision. The system dynamically identifies critical nets and adjusts cluster formations accordingly.
Solution Approach 2:
The patent introduces clusters as intermediary structures between individual cells and the final placement layout. These clusters act as intermediate units that preserve strong net connections while enabling systematic placement optimization. The cluster concept serves as a mediator that maintains electrical relationship integrity (precision) while allowing automated algorithmic processing (ease of operation) at a higher level of abstraction.
3Ease of manufacture
If cells are placed without optimization to simplify the design process, then the ease of manufacture is improved, but the area and wiring length increase
Solution Approach 1:
The patent segments the placement problem into cluster formation and cluster positioning, allowing simplified automated processing at the cluster level while achieving optimized area utilization through the clustering structure. Cells within each cluster are naturally grouped based on net connections, reducing the need for complex cross-cluster wiring and minimizing overall circuit area.
Solution Approach 2:
The patent merges cells with strong net connections into clusters, combining them into unified placement units. This merging reduces the total wiring length by keeping connected cells close together, while the cluster-based approach maintains design process simplicity through systematic automated handling. The merging of related cells into clusters naturally minimizes area usage without requiring complex manual optimization.
4Length of stationary object
If cells with strong relations are grouped together to reduce wiring length, then the area and wiring length are reduced, but the device complexity of the clustering algorithm increases
Solution Approach 1:
The patent performs preliminary analysis of net connections to identify strong relationships between cells before forming clusters. This preliminary action involves analyzing the netlist to determine which cells have critical connections, allowing the clustering algorithm to focus on grouping these specifically identified cells. By pre-identifying strong relationships, the algorithm reduces the complexity of the clustering process while still achieving effective wiring length reduction.
Solution Approach 2:
The patent applies different processing strategies to different parts of the circuit based on local characteristics. Cells with strong net connections are grouped into tight clusters with specific placement constraints, while other cells are placed more flexibly. This local quality approach allows the algorithm to concentrate computational effort where it matters most (in clusters with strong connections) while simplifying processing for other areas, thereby reducing overall algorithmic complexity while achieving wiring optimization.
Data Source
AI summary
Provided are a system for designing a semiconductor circuit and an operating method of the same. The system includes a working memory loading a clustering application for generating a cluster, based on instances respectively corresponding to cells of the semiconductor circuit, and loading a design tool for placing the cells. The clustering application, when an output terminal of a first instance is connected to a second instance and the number of instances connected to the output terminal of the first instance is one, classifies the first instance and the second instance into a candidate group pair. The clustering application, when all instances connected to an input terminal of the second instance are classified into the candidate group pair with the second instance, generates the cluster including the first instance and the second instance.