  We introduce a new method for inverse design of nanophotonic devices which
guarantees that resulting designs satisfy strict length scale constraints -
including minimum width and spacing constraints required by commercial
semiconductor foundries. The method adopts several concepts from machine
learning to transform the problem of topology optimization with strict length
scale constraints to an unconstrained stochastic gradient optimization problem.
Specifically, we introduce a conditional generator for feasible designs and
adopt a straight-through estimator for backpropagation of gradients to a latent
design. We demonstrate the performance and reliability of our method by
designing several common integrated photonic components.
