MCE-HGCN: Heterogeneous Graph Convolution Network for Analog IC Matching Constraints Extraction
Matching constraints in an analog integrated circuit (IC) are critical to optimizing layout performance. To extract these matching constraints accurately and efficiently from the netlist, we propose the heterogeneous matching constraint extraction graph neural network (MCE-HGCN). First, the netlist...
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2025-06-01
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| author | Yong Zhang Yong Yin Ning Xu Bowen Jia |
| author_facet | Yong Zhang Yong Yin Ning Xu Bowen Jia |
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| description | Matching constraints in an analog integrated circuit (IC) are critical to optimizing layout performance. To extract these matching constraints accurately and efficiently from the netlist, we propose the heterogeneous matching constraint extraction graph neural network (MCE-HGCN). First, the netlist is mapped into a heterogeneous attribute multi-graph, and based on the characteristics of analog IC matching constraints, a mixed-domain attention mechanism is developed to leverage both the topology information and node attributes in the graph to characterize node embeddings. A matching classifier, implemented using the support vector machine (SVM), is then employed to classify different types of matching constraints from the netlist. Additionally, a matching filter is introduced to remove interference terms. Experimental results demonstrate that the MCE-HGCN model converges effectively with small datasets. In the matching prediction process, the mean <i>F</i>1 score reached 0.917 across different netlist processes and circuit types while maintaining a shorter runtime compared to other methods. Ablation experiments also show that incorporating the mixed-domain attention mechanism and the matching filter individually leads to significant performance improvements. Overall, MCE-HGCN excels at extracting matching constraints from various analog circuits and processes, offering valuable insights for placement guidance and enhancing the efficiency of analog IC layout design. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-06-01 |
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| series | Micromachines |
| spelling | doaj-art-d508b6f4eddf48d79a0494d450fb598a2025-08-20T03:29:49ZengMDPI AGMicromachines2072-666X2025-06-0116667710.3390/mi16060677MCE-HGCN: Heterogeneous Graph Convolution Network for Analog IC Matching Constraints ExtractionYong Zhang0Yong Yin1Ning Xu2Bowen Jia3School of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaMatching constraints in an analog integrated circuit (IC) are critical to optimizing layout performance. To extract these matching constraints accurately and efficiently from the netlist, we propose the heterogeneous matching constraint extraction graph neural network (MCE-HGCN). First, the netlist is mapped into a heterogeneous attribute multi-graph, and based on the characteristics of analog IC matching constraints, a mixed-domain attention mechanism is developed to leverage both the topology information and node attributes in the graph to characterize node embeddings. A matching classifier, implemented using the support vector machine (SVM), is then employed to classify different types of matching constraints from the netlist. Additionally, a matching filter is introduced to remove interference terms. Experimental results demonstrate that the MCE-HGCN model converges effectively with small datasets. In the matching prediction process, the mean <i>F</i>1 score reached 0.917 across different netlist processes and circuit types while maintaining a shorter runtime compared to other methods. Ablation experiments also show that incorporating the mixed-domain attention mechanism and the matching filter individually leads to significant performance improvements. Overall, MCE-HGCN excels at extracting matching constraints from various analog circuits and processes, offering valuable insights for placement guidance and enhancing the efficiency of analog IC layout design.https://www.mdpi.com/2072-666X/16/6/677analog ICmatching constraintsheterogeneous multi-graphmixed attentionssmall dataset |
| spellingShingle | Yong Zhang Yong Yin Ning Xu Bowen Jia MCE-HGCN: Heterogeneous Graph Convolution Network for Analog IC Matching Constraints Extraction Micromachines analog IC matching constraints heterogeneous multi-graph mixed attentions small dataset |
| title | MCE-HGCN: Heterogeneous Graph Convolution Network for Analog IC Matching Constraints Extraction |
| title_full | MCE-HGCN: Heterogeneous Graph Convolution Network for Analog IC Matching Constraints Extraction |
| title_fullStr | MCE-HGCN: Heterogeneous Graph Convolution Network for Analog IC Matching Constraints Extraction |
| title_full_unstemmed | MCE-HGCN: Heterogeneous Graph Convolution Network for Analog IC Matching Constraints Extraction |
| title_short | MCE-HGCN: Heterogeneous Graph Convolution Network for Analog IC Matching Constraints Extraction |
| title_sort | mce hgcn heterogeneous graph convolution network for analog ic matching constraints extraction |
| topic | analog IC matching constraints heterogeneous multi-graph mixed attentions small dataset |
| url | https://www.mdpi.com/2072-666X/16/6/677 |
| work_keys_str_mv | AT yongzhang mcehgcnheterogeneousgraphconvolutionnetworkforanalogicmatchingconstraintsextraction AT yongyin mcehgcnheterogeneousgraphconvolutionnetworkforanalogicmatchingconstraintsextraction AT ningxu mcehgcnheterogeneousgraphconvolutionnetworkforanalogicmatchingconstraintsextraction AT bowenjia mcehgcnheterogeneousgraphconvolutionnetworkforanalogicmatchingconstraintsextraction |