1. From electrons to photons: Quantum- inspired modeling in nanophotonics Steven G. Johnson, MIT Applied Mathematics

2. Nano-photonic media ( -scale) synthetic materials strange waveguides 3d structures hollow-core fibers optical phenomena & microcavities [B. Norris, UMN] [Assefa & Kolodziejski, MIT] [Mangan, Corning]

3. 18871987 Photonic Crystals periodic electromagnetic media can have a band gap: optical “insulators”

4. Electronic and Photonic Crystals atoms in diamond structure wavevector electron energy Periodic Medium Bloch waves: Band Diagram dielectric spheres, diamond lattice wavevector photon frequency interacting: hard problemnon-interacting: easy problem

5. Electronic & Photonic Modelling ElectronicPhotonic strongly interacting —tricky approximations non-interacting (or weakly), —simple approximations (finite resolution) —any desired accuracy lengthscale dependent (from Planck’s h) scale-invariant —e.g. size  10   10 Option 1: Numerical “experiments” — discretize time & space … go Option 2: Map possible states & interactions using symmetries and conservation laws: band diagram

6. Fun with Math 0 dielectric function  (x) = n 2 (x) First task: get rid of this mess eigen-operatoreigen-value eigen-state + constraint

7. Electronic & Photonic Eigenproblems ElectronicPhotonic simple linear eigenproblem (for linear materials) nonlinear eigenproblem (V depends on e density |  | 2 ) —many well-known computational techniques Hermitian = real E & , … Periodicity = Bloch’s theorem…

8. A 2d Model System square lattice, period a dielectric “atom”  =12 (e.g. Si) a a E H TM

9. Periodic Eigenproblems if eigen-operator is periodic, then Bloch-Floquet theorem applies: can choose: periodic “envelope” planewave Corollary 1: k is conserved, i.e. no scattering of Bloch wave Corollary 2: given by finite unit cell, so  are discrete  n (k)

10. Solving the Maxwell Eigenproblem H(x,y) e i(k  x –  t) where: constraint: 1 Want to solve for  n (k), & plot vs. “all” k for “all” n, Finite cell  discrete eigenvalues  n Limit range of k : irreducible Brillouin zone 2 Limit degrees of freedom: expand H in finite basis 3 Efficiently solve eigenproblem: iterative methods

11. Solving the Maxwell Eigenproblem: 1 1 Limit range of k : irreducible Brillouin zone 2 Limit degrees of freedom: expand H in finite basis 3 Efficiently solve eigenproblem: iterative methods —Bloch’s theorem: solutions are periodic in k kxkx kyky first Brillouin zone = minimum |k| “primitive cell”  M X irreducible Brillouin zone: reduced by symmetry

12. Solving the Maxwell Eigenproblem: 2a 1 Limit range of k : irreducible Brillouin zone 2 Limit degrees of freedom: expand H in finite basis (N) 3 Efficiently solve eigenproblem: iterative methods solve: finite matrix problem:

13. Solving the Maxwell Eigenproblem: 2b 1 Limit range of k : irreducible Brillouin zone 2 Limit degrees of freedom: expand H in finite basis 3 Efficiently solve eigenproblem: iterative methods — must satisfy constraint: Planewave (FFT) basis constraint: uniform “grid,” periodic boundaries, simple code, O(N log N) Finite-element basis constraint, boundary conditions: Nédélec elements [ Nédélec, Numerische Math. 35, 315 (1980) ] nonuniform mesh, more arbitrary boundaries, complex code & mesh, O(N) [ figure: Peyrilloux et al., J. Lightwave Tech. 21, 536 (2003) ]

14. Solving the Maxwell Eigenproblem: 3a 1 Limit range of k : irreducible Brillouin zone 2 Limit degrees of freedom: expand H in finite basis 3 Efficiently solve eigenproblem: iterative methods Faster way: — start with initial guess eigenvector h 0 — iteratively improve — O(Np) storage, ~ O(Np 2 ) time for p eigenvectors Slow way: compute A & B, ask LAPACK for eigenvalues — requires O(N 2 ) storage, O(N 3 ) time (p smallest eigenvalues)

15. Solving the Maxwell Eigenproblem: 3b 1 Limit range of k : irreducible Brillouin zone 2 Limit degrees of freedom: expand H in finite basis 3 Efficiently solve eigenproblem: iterative methods Many iterative methods: — Arnoldi, Lanczos, Davidson, Jacobi-Davidson, …, Rayleigh-quotient minimization

16. Solving the Maxwell Eigenproblem: 3c 1 Limit range of k : irreducible Brillouin zone 2 Limit degrees of freedom: expand H in finite basis 3 Efficiently solve eigenproblem: iterative methods Many iterative methods: — Arnoldi, Lanczos, Davidson, Jacobi-Davidson, …, Rayleigh-quotient minimization for Hermitian matrices, smallest eigenvalue  0 minimizes: minimize by preconditioned conjugate-gradient (or…) “variational theorem”

17. Band Diagram of 2d Model System (radius 0.2a rods,  =12) E H TM a frequency  (2πc/a) = a /  X M  XM  irreducible Brillouin zone gap for n > ~1.75:1

18. Origin of the Band Gap Hermitian eigenproblems: solutions are orthogonal and satisfy a variational theorem ElectronicPhotonic minimize kinetic + potential energy (e.g. “bonding” state) minimize: field oscillations field in high  higher bands orthogonal to lower — must oscillate (high kinetic) or be in low  (high potential) (e.g. “anti-bonding” state)

19. Origin of Gap in 2d Model System E H TM  XM  EzEz –+ EzEz gap for n > ~1.75:1 lives in high  orthogonal: node in high 

20. The Iteration Scheme is Important (minimizing function of 10 4 –10 8 + variables!) Steepest-descent: minimize (h +  f) over  … repeat Conjugate-gradient: minimize (h +  d) — d is  f + (stuff): conjugate to previous search dirs Preconditioned steepest descent: minimize (h +  d) — d = (approximate A -1 )  f ~ Newton’s method Preconditioned conjugate-gradient: minimize (h +  d) — d is (approximate A -1 ) [  f + (stuff)]

21. The Iteration Scheme is Important (minimizing function of ~40,000 variables) # iterations % error preconditioned conjugate-gradient no conjugate-gradient no preconditioning

22. The Boundary Conditions are Tricky  E || is continuous E  is discontinuous (D  =  E  is continuous) Any single scalar  fails: (mean D) ≠ (any  ) (mean E) Use a tensor  E || EE

23. The  -averaging is Important resolution (pixels/period) % error backwards averaging tensor averaging no averaging correct averaging changes order of convergence from ∆x to ∆x 2 (similar effects in other E&M numerics & analyses)

24. Gap, Schmap? a frequency   XM  But, what can we do with the gap?

25. Intentional “defects” are good microcavities waveguides (“wires”)

26. Intentional “defects” in 2d (Same computation, with supercell = many primitive cells)

27. Microcavity Blues For cavities (point defects) frequency-domain has its drawbacks: Best methods compute lowest-  bands, but N d supercells have N d modes below the cavity mode — expensive Best methods are for Hermitian operators, but losses requires non-Hermitian

28. Time-Domain Eigensolvers (finite-difference time-domain = FDTD) Simulate Maxwell’s equations on a discrete grid, + absorbing boundaries (leakage loss) Excite with broad-spectrum dipole ( ) source  Response is many sharp peaks, one peak per mode complex  n [ Mandelshtam, J. Chem. Phys. 107, 6756 (1997) ] signal processing decay rate in time gives loss

29. Signal Processing is Tricky complex  n ? signal processing Decaying signal (t) Lorentzian peak (  ) FFT a common approach: least-squares fit of spectrum fit to:

30. Fits and Uncertainty Portion of decaying signal (t) Unresolved Lorentzian peak (  ) actual signal portion problem: have to run long enough to completely decay There is a better way, which gets complex  to > 10 digits

31. Unreliability of Fitting Process  = 1+0.033i  = 1.03+0.025i sum of two peaks Resolving two overlapping peaks is near-impossible 6-parameter nonlinear fit (too many local minima to converge reliably) Sum of two Lorentzian peaks (  ) There is a better way, which gets complex  for both peaks to > 10 digits

32. Quantum-inspired signal processing (NMR spectroscopy): Filter-Diagonalization Method (FDM) [ Mandelshtam, J. Chem. Phys. 107, 6756 (1997) ] Given time series y n, write: …find complex amplitudes a k & frequencies  k by a simple linear-algebra problem! Idea: pretend y(t) is autocorrelation of a quantum system: say: time-∆t evolution-operator:

33. Filter-Diagonalization Method (FDM) [ Mandelshtam, J. Chem. Phys. 107, 6756 (1997) ] We want to diagonalize U: eigenvalues of U are e i  ∆t …expand U in basis of |  (n∆t)>: U mn given by y n ’s — just diagonalize known matrix!

34. Filter-Diagonalization Summary [ Mandelshtam, J. Chem. Phys. 107, 6756 (1997) ] U mn given by y n ’s — just diagonalize known matrix! A few omitted steps: —Generalized eigenvalue problem (basis not orthogonal) —Filter y n ’s (Fourier transform): small bandwidth = smaller matrix (less singular) resolves many peaks at once # peaks not known a priori resolve overlapping peaks resolution >> Fourier uncertainty

35. Do try this at home Bloch-mode eigensolver: http://ab-initio.mit.edu/mpb/ Filter-diagonalization: http://ab-initio.mit.edu/harminv/ Photonic-crystal tutorials (+ THIS TALK): http://ab-initio.mit.edu/ /photons/tutorial/