Semiconductor Placement Evaluation Using RL Clustering Rewards
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Solution Overview
Problem
The current semiconductor design process is labor-intensive and varies in quality based on engineer experience, with high complexity in evaluating the placement of tens to millions of semiconductor devices, leading to inefficiencies and increased time and cost.
Innovation Solution
A reinforcement learning-based method that clusters semiconductor devices based on connection information, converts hypergraph structures to ordinary graphs, and trains a neural network model to optimize placement by determining rewards for wire length and congestion, reducing the complexity of the evaluation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If reinforcement learning with clustering is applied to evaluate semiconductor device placement, then evaluation complexity is reduced, but the accuracy of placement evaluation may be compromised due to clustering approximations
Solution Approach 1:
The patent divides the semiconductor devices into multiple clusters based on spatial proximity and connection relationships. Each cluster is evaluated as a group rather than individually, segmenting the overall evaluation problem into smaller sub-problems that can be processed more efficiently by the reinforcement learning model.
Solution Approach 2:
The patent creates a simplified representative model of each cluster that captures the essential characteristics (wire length, congestion, connection patterns) without requiring detailed evaluation of every individual device within the cluster. This copying approach maintains evaluation accuracy while reducing computational complexity.
2Loss of time
If the number of semiconductor devices to be evaluated is reduced through clustering, then evaluation time is reduced, but the detail information of individual device connections may be lost
Solution Approach 1:
The patent preserves detailed connection information within each cluster by maintaining local evaluation metrics such as intra-cluster wire length and congestion. While clusters are evaluated as groups, the internal structure and connection details are not completely aggregated away but rather preserved at the local level for accurate assessment.
Solution Approach 2:
The patent evaluates clusters at multiple levels: the cluster level for overall placement quality and the device level within clusters for detailed connection information. This multi-dimensional evaluation approach allows simultaneous reduction of evaluation time through clustering while preserving necessary detail information through hierarchical assessment.
3Productivity
If neural network model training is performed with clustered data, then training efficiency is improved, but the model's ability to capture fine-grained placement patterns may be reduced
Solution Approach 1:
The patent employs a dynamic evaluation approach where the reinforcement learning model adapts its evaluation granularity based on the placement stage and device type. During training, the model learns to evaluate both cluster-level patterns and device-level details as needed, dynamically adjusting the level of detail examined rather than using a fixed granularity throughout the training process.
Solution Approach 2:
The patent combines cluster-level evaluation metrics (wire length, congestion, density) with device-level connection information in the training data fed to the neural network. This merging of aggregated and detailed information allows the model to learn from both the big picture placement patterns and fine-grained connection relationships simultaneously, improving both training efficiency and pattern recognition accuracy.
Data Source
AI summary
Disclosed is a method of evaluating placement of semiconductor devices performed by a computing device according to an exemplary embodiment of the present disclosure. The method includes receiving connection information representing a connection relationship between semiconductor devices; clustering the semiconductor devices based on the connection information; and determining a reward to train a neural network model based on clustering.