Computational Bounds for Photonic Design

Physical design problems, such as photonic inverse design, are typically solved using local optimization methods. These methods often produce what appear to be good or very good designs when compared to classical design methods, but it is not known how far from optimal such designs really are. We address this issue by developing methods for computing a bound on the true optimal value of a physical design problem; physical designs with objective smaller than our bound are impossible to achieve. Our bound is based on Lagrange duality and exploits the special mathematical structure of these physical design problems. For a multi-mode 2D Helmholtz resonator, numerical examples show that the bounds we compute are often close to the objective values obtained using local optimization methods, which reveals that the designs are not only good, but in fact nearly optimal. Our computational bounding method also produces, as a by-product, a reasonable starting point for local optimization methods.

[1]  W. Chew Waves and Fields in Inhomogeneous Media , 1990 .

[2]  R. Haftka,et al.  Elements of Structural Optimization , 1984 .

[3]  Stephen P. Boyd,et al.  Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding , 2013, Journal of Optimization Theory and Applications.

[4]  Eddie Wadbro,et al.  An efficient loudspeaker horn designed by numerical optimization : An experimental study , 2010 .

[5]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[6]  E. Purcell Life at Low Reynolds Number , 2008 .

[7]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[8]  Raquel Reis,et al.  Applications of Second Order Cone Programming , 2013 .

[9]  Wenbo Gao,et al.  ADMM for multiaffine constrained optimization , 2018, Optim. Methods Softw..

[10]  Steven G. Johnson,et al.  Fundamental limits to optical response in absorptive systems. , 2015, Optics express.

[11]  C. Barus AN AMERICAN JOURNAL OF PHYSICS. , 1902, Science.

[12]  D. Whiffen Thermodynamics , 1973, Nature.

[13]  Jesse Lu,et al.  Nanophotonic computational design. , 2013, Optics express.

[14]  Max Born,et al.  Principles of optics - electromagnetic theory of propagation, interference and diffraction of light (7. ed.) , 1999 .

[15]  Steven G. Johnson,et al.  Fundamental Limits to Near-Field Optical Response over Any Bandwidth , 2018, Physical Review X.

[16]  Audra E. Kosh,et al.  Linear Algebra and its Applications , 1992 .

[17]  Eli Yablonovitch,et al.  Adjoint shape optimization applied to electromagnetic design. , 2013, Optics express.

[18]  Jesse Lu,et al.  Inverse design of nanophotonic structures using complementary convex optimization , 2009, CLEO/QELS: 2010 Laser Science to Photonic Applications.

[19]  Stephen Boyd,et al.  A Rewriting System for Convex Optimization Problems , 2017, ArXiv.

[20]  Stephen P. Boyd,et al.  ECOS: An SOCP solver for embedded systems , 2013, 2013 European Control Conference (ECC).

[21]  M. Born Principles of Optics : Electromagnetic theory of propagation , 1970 .

[22]  Alan Edelman,et al.  Julia: A Fresh Approach to Numerical Computing , 2014, SIAM Rev..

[23]  Lisa Turner,et al.  Applications of Second Order Cone Programming , 2012 .

[24]  Jelena Vucković,et al.  Inverse design in nanophotonics , 2018, Nature Photonics.

[25]  Iain Dunning,et al.  JuMP: A Modeling Language for Mathematical Optimization , 2015, SIAM Rev..