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Optimization of Bandpass Filter Design Using Parallel Neural Space-Mapping Techniques

This study presents a novel approach to optimizing electromagnetic (EM)-based design with a focus on bandpass filters using Parallel Neural Space-Mapping (NSM) methods. By training NSM with 13 sets of data derived from coarse and fine models, we demonstrate iterative improvements in filter performance across several optimization iterations. The design specifications include passband and stopband criteria, showcasing the effectiveness of this technique for precise adjustments. We also address the challenges of diminishing returns with accuracy and the careful adjustments made in the later iterations.

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Optimization of Bandpass Filter Design Using Parallel Neural Space-Mapping Techniques

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  1. Parallel Neural Space-Mapping (NSM)Optimization for EM-Based Design Zhang Chao

  2. Train NSM with 2n+1 sets of data

  3. Example：A BandpassFilter Coarse Model: Fine Model:

  4. Example：A Bandpass Filter A 5% deviation from X for L and S is used,So there will be 13 sets of data for one iteration. use Openmp method to get the training data

  5. Example：A Bandpass Filter S21 Coarse model Lc1,Lc2,Lc3,Sc1,Sc2,Sc3 fc Fmapping(w) Coarse model f L1,L2,L3,S1,S2,S3 freq X:the input of neural SM model NSM model

  6. Example：A BandpassFilter S21 Coarse model Lc1,Lc2,Lc3,Sc1,Sc2,Sc3 fc Fmapping(w) Coarse model f L1,L2,L3,S1,S2,S3 freq X:the input of neural SM model

  7. Example：A BandpassFilter Design Specification: In the passband(4.008GHz-4.058GHz) In the stopband(<3.967GHz,>4.099GHz)

  8. Example：A Bandpass Filter The initial state: The S21 of Coarse Model The S21 of Fine Model

  9. Example：A Bandpass Filter

  10. Iteration 1: before training

  11. Iteration 1: after training

  12. Iteration 1: value the solution in CST Before optimization After optimization

  13. Iteration 2: before training

  14. Iteration 2: after training

  15. Iteration 2: value the solution in CST Before optimization After optimization

  16. Iteration 3: before training

  17. Iteration 3: after training

  18. Iteration 3: value the solution in CST Before optimization After optimization

  19. Iteration 4: before training

  20. Iteration 4: after training

  21. Iteration 4: value the solution in CST Before optimization After optimization

  22. Iteration 5: before training

  23. Iteration 5: after training

  24. Iteration 5: value the solution in CST Before optimization After optimization

  25. A shortcoming of the method When the error becomes very little, the effect of the method will become very little at the same time. It takes many iterations to let the error disappeared. So, in the fifth iteration I make the specification more strict.

  26. Example：A BandpassFilter Summary:

  27. Thank you！

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