  Length scale control is imposed in topology optimization (TO) to make designs
amenable to manufacturing and other functional requirements. Broadly, there are
two types of length-scale control in TO: \emph {exact} and \emph {approximate}.
While the former is desirable, its implementation can be difficult, and is
computationally expensive. Approximate length scale control is therefore
preferred, and is often sufficient for early stages of design.
  In this paper we propose an approximate length scale control strategy for TO,
by extending a recently proposed density-based TO formulation using neural
networks (TOuNN). Specifically, we enhance TOuNN with a Fourier space
projection, to control the minimum and/or maximum length scales. The proposed
method does not involve additional constraints, and the sensitivity
computations are automated by expressing the computations in an end-end
differentiable fashion using the neural net's library. The proposed method is
illustrated through several numerical experiments for single and multi-material
designs.
