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1. Design of Photonic Crystals with Multiple and Combined Band Gaps, plus Fabrication-Robust Design R. Freund, H. Men, C. Nguyen, P. Parrilo and J. Peraire MIT AFOSR, April 2011 MASSACHUSETTS INSTITUTE OF TECHNOLOGY

2. 1887 - Rayleigh 1987 – Yablonovitch & John Wave Propagation in Periodic Media scattering For most l, beam(s) propagate through crystal without scattering (scattering cancels coherently). But for some l(~ 2a), no light can propagate: a band gap ( from S.G. Johnson ) MASSACHUSETTS INSTITUTE OF TECHNOLOGY

3. Photonic Crystals 3D Crystals Band Gap: Objective S. G. Johnson et al., Appl. Phys. Lett. 77, 3490 (2000) Applications • By introducing “imperfections” • one can develop: • Waveguides • Hyperlens • Resonant cavities • Switches • Splitters • … Mangan, et al., OFC 2004 PDP24 MASSACHUSETTS INSTITUTE OF TECHNOLOGY

4. The Optimal Design Problem for Photonic Crystals A photonic crystal with optimized 7th band gap. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

5. The Optimal Design Problem for Photonic Crystals MASSACHUSETTS INSTITUTE OF TECHNOLOGY

6. The Optimal Design Problem for Photonic Crystals MASSACHUSETTS INSTITUTE OF TECHNOLOGY

7. ? The Optimal Design Problem for Photonic Crystals MASSACHUSETTS INSTITUTE OF TECHNOLOGY

8. Previous Work • There are some approaches proposed for solving the band gap optimization problem: • Cox and Dobson (2000) first considered the mathematical optimization of the band gap problem and proposed a projected generalized gradient ascent method. • Sigmund and Jensen (2003) combined topology optimization with the method of moving asymptotes (Svanberg (1987)). • Kao, Osher, and Yablonovith (2005) used “the level set” method with a generalized gradient ascent method. • However, these earlier proposals are gradient-based methods and use eigenvalues as explicit functions. They suffer from the low regularity of the problem due to eigenvalue multiplicity. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

9. Our Approach • Replace original eigenvalue formulation by a subspace method, and • Convert the subspace problem to a convex semidefinite program (SDP) via semi-definite inclusion and linearization. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

10. First Step: Standard Discretizaton MASSACHUSETTS INSTITUTE OF TECHNOLOGY

11. Optimization Formulation: P0 Typically, MASSACHUSETTS INSTITUTE OF TECHNOLOGY

12. Parameter Dependence MASSACHUSETTS INSTITUTE OF TECHNOLOGY

13. Change of Decision Variables MASSACHUSETTS INSTITUTE OF TECHNOLOGY

14. Reformulating the problem: P1 We demonstrate the reformulation in the TE case, This reformulation is exact, but non-convex and large-scale. We use a subspace method to reduce the problem size. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

15. Subspace Method MASSACHUSETTS INSTITUTE OF TECHNOLOGY

16. Subspace Method, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY

17. Subspace Method, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY

18. Linear Fractional SDP : MASSACHUSETTS INSTITUTE OF TECHNOLOGY

19. Solution Procedure The SDP optimization problems can be solved efficiently using modern interior-point methods [Toh et al. (1999)]. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

20. Results: Optimal Structures MASSACHUSETTS INSTITUTE OF TECHNOLOGY

21. Results: Computation Time All computation performed on a Linux PC with Dual Core AMD Opteron 270, 2.0 GHz. The eigenvalue problem is solved by using the ARPACK software. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

22. Mesh Adaptivity : Refinement Criteria • Interface elements • 2:1 rule MASSACHUSETTS INSTITUTE OF TECHNOLOGY

23. Mesh Adaptivity : Solution Procedure MASSACHUSETTS INSTITUTE OF TECHNOLOGY

24. Results: Optimization Process MASSACHUSETTS INSTITUTE OF TECHNOLOGY

25. Results: Computation Time MASSACHUSETTS INSTITUTE OF TECHNOLOGY

26. Multiple Band Gap Optimization Multiple band gap optimization is a natural extension of the previous single band gap optimization that seeks to maximize the minimum of weighted gap-midgap ratios: MASSACHUSETTS INSTITUTE OF TECHNOLOGY

27. Multiple Band Gap Optimization Even with other things being the same (e.g., discretization, change of variables, subspace construction), the multiple band gap optimization problem cannot be reformulated as a linear fractional SDP. We start with: where MASSACHUSETTS INSTITUTE OF TECHNOLOGY

28. Multiple Band Gap Optimization This linearizes to: where MASSACHUSETTS INSTITUTE OF TECHNOLOGY

29. Solution Procedure MASSACHUSETTS INSTITUTE OF TECHNOLOGY

30. Sample Computational Results: 2nd and 5th TM Band Gaps MASSACHUSETTS INSTITUTE OF TECHNOLOGY

31. Sample Computational Results: 2nd and 5th TM Band Gaps MASSACHUSETTS INSTITUTE OF TECHNOLOGY

32. Optimized Structures • TE polarization, • Triangular lattice, • 1st and 3rd band gaps • TEM polarizations, • Square lattice, • Complete band gaps, • 9th and 12th band gaps MASSACHUSETTS INSTITUTE OF TECHNOLOGY

33. Results: Pareto Frontiers of 1st and 3rd TE Band Gaps • TE polarization MASSACHUSETTS INSTITUTE OF TECHNOLOGY

34. Results: Computational Time Square lattice Execution Time (minutes) MASSACHUSETTS INSTITUTE OF TECHNOLOGY

35. Future work • Design of 3-dimensional photonic crystals, • Incorporate robust fabrication issues, see next slides…. • Metamaterial designs for cloaking devices, superlenses, and shockwave mitigation. • Band-gap design optimization for other wave propagation problems, e.g., acoustic, elastic … • Non-periodic structures, e.g., photonic crystal fibers. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

36. Need for Fabrication Robustness MASSACHUSETTS INSTITUTE OF TECHNOLOGY

37. Constructing Fabricable Solutions MASSACHUSETTS INSTITUTE OF TECHNOLOGY

38. Constructing Fabricable Solutions, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY

39. Quality of User-Constructed Solution MASSACHUSETTS INSTITUTE OF TECHNOLOGY

40. Quality of User-Constructed Solution, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY

41. Fabrication Robustness Paradigm MASSACHUSETTS INSTITUTE OF TECHNOLOGY

42. Fabrication Robustness Paradigm, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY

43. Basic Results MASSACHUSETTS INSTITUTE OF TECHNOLOGY

44. Fabrication Robustness: Basic Model MASSACHUSETTS INSTITUTE OF TECHNOLOGY

45. Computable FR Problems via Special Structure MASSACHUSETTS INSTITUTE OF TECHNOLOGY

46. Computable FR Problems via Special Structure, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY

47. Computable FR Problems via Special Structure, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY