  We present a novel approach based on machine learning for designing photonic
structures. In particular, we focus on strong light confinement that allows the
design of an efficient free-space-to-waveguide coupler which is made of Si-
slab overlying on the top of silica substrate. The learning algorithm is
implemented using bitwise square Si- cells and the whole optimized device has a
footprint of $\boldsymbol{2 \, \mu m \times 1\, \mu m}$, which is the smallest
size ever achieved numerically. To find the effect of Si- slab thickness on the
sub-wavelength focusing and strong coupling characteristics of optimized
photonic structure, we carried out three-dimensional time-domain numerical
calculations. Corresponding optimum values of full width at half maximum and
coupling efficiency were calculated as $\boldsymbol{0.158 \lambda}$ and
$\boldsymbol{-1.87\,dB}$ with slab thickness of $\boldsymbol{280nm}$. Compared
to the conventional counterparts, the optimized lens and coupler designs are
easy-to-fabricate via optical lithography techniques, quite compact, and can
operate at telecommunication wavelengths. The outcomes of the presented study
show that machine learning can be beneficial for efficient photonic designs in
various potential applications such as polarization-division, beam manipulation
and optical interconnects.
