A Multi-Point Moment Matching Approach with Frequency-Aware ROM-Based Criteria for RLCk Model Order Reduction
Model order reduction (MOR) is crucial for efficiently simulating large-scale RLCk models extracted from modern integrated circuits. Among MOR methods, balanced truncation offers strong theoretical error bounds but is computationally intensive and does not preserve passivity. In contrast, moment mat...
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MDPI AG
2025-06-01
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| author | Dimitrios Garyfallou Christos Giamouzis Nestor Evmorfopoulos |
| author_facet | Dimitrios Garyfallou Christos Giamouzis Nestor Evmorfopoulos |
| author_sort | Dimitrios Garyfallou |
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| description | Model order reduction (MOR) is crucial for efficiently simulating large-scale RLCk models extracted from modern integrated circuits. Among MOR methods, balanced truncation offers strong theoretical error bounds but is computationally intensive and does not preserve passivity. In contrast, moment matching (MM) techniques are widely adopted in industrial tools due to their computational efficiency and ability to preserve passivity in RLCk models. Typically, MM approaches based on the rational Krylov subspace (RKS) are employed to produce reduced-order models (ROMs). However, the quality of the reduction is influenced by the selection of the number of moments and expansion points, which can be challenging to determine. This underlines the need for advanced strategies and reliable convergence criteria to adaptively control the reduction process and ensure accurate ROMs. This article introduces a frequency-aware multi-point MM (MPMM) method that adaptively constructs an RKS by closely monitoring the ROM transfer function. The proposed approach features automatic expansion point selection, local and global convergence criteria, and efficient implementation techniques. Compared to an established MM technique, MPMM achieves up to 16.3× smaller ROMs for the same accuracy, over 99.18% reduction in large-scale benchmarks, and up to 4× faster runtime. These advantages establish MPMM as a strong candidate for integration into industrial parasitic extraction tools. |
| format | Article |
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| institution | Kabale University |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-2e36b75dd504470ba86fdcace3cbc42e2025-08-20T03:32:16ZengMDPI AGTechnologies2227-70802025-06-0113727410.3390/technologies13070274A Multi-Point Moment Matching Approach with Frequency-Aware ROM-Based Criteria for RLCk Model Order ReductionDimitrios Garyfallou0Christos Giamouzis1Nestor Evmorfopoulos2Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, GreeceModel order reduction (MOR) is crucial for efficiently simulating large-scale RLCk models extracted from modern integrated circuits. Among MOR methods, balanced truncation offers strong theoretical error bounds but is computationally intensive and does not preserve passivity. In contrast, moment matching (MM) techniques are widely adopted in industrial tools due to their computational efficiency and ability to preserve passivity in RLCk models. Typically, MM approaches based on the rational Krylov subspace (RKS) are employed to produce reduced-order models (ROMs). However, the quality of the reduction is influenced by the selection of the number of moments and expansion points, which can be challenging to determine. This underlines the need for advanced strategies and reliable convergence criteria to adaptively control the reduction process and ensure accurate ROMs. This article introduces a frequency-aware multi-point MM (MPMM) method that adaptively constructs an RKS by closely monitoring the ROM transfer function. The proposed approach features automatic expansion point selection, local and global convergence criteria, and efficient implementation techniques. Compared to an established MM technique, MPMM achieves up to 16.3× smaller ROMs for the same accuracy, over 99.18% reduction in large-scale benchmarks, and up to 4× faster runtime. These advantages establish MPMM as a strong candidate for integration into industrial parasitic extraction tools.https://www.mdpi.com/2227-7080/13/7/274model order reductionmoment matchingrational Krylov subspacecircuit simulation |
| spellingShingle | Dimitrios Garyfallou Christos Giamouzis Nestor Evmorfopoulos A Multi-Point Moment Matching Approach with Frequency-Aware ROM-Based Criteria for RLCk Model Order Reduction Technologies model order reduction moment matching rational Krylov subspace circuit simulation |
| title | A Multi-Point Moment Matching Approach with Frequency-Aware ROM-Based Criteria for RLCk Model Order Reduction |
| title_full | A Multi-Point Moment Matching Approach with Frequency-Aware ROM-Based Criteria for RLCk Model Order Reduction |
| title_fullStr | A Multi-Point Moment Matching Approach with Frequency-Aware ROM-Based Criteria for RLCk Model Order Reduction |
| title_full_unstemmed | A Multi-Point Moment Matching Approach with Frequency-Aware ROM-Based Criteria for RLCk Model Order Reduction |
| title_short | A Multi-Point Moment Matching Approach with Frequency-Aware ROM-Based Criteria for RLCk Model Order Reduction |
| title_sort | multi point moment matching approach with frequency aware rom based criteria for rlck model order reduction |
| topic | model order reduction moment matching rational Krylov subspace circuit simulation |
| url | https://www.mdpi.com/2227-7080/13/7/274 |
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