  In this work, we demonstrate three ultra-compact integrated-photonics
devices, which are designed via a machine-learning algorithm coupled with
finite-difference time-domain (FDTD) modeling. Through digitizing the design
domain into "binary pixels" these digital metamaterials are readily
manufacturable as well. By showing a variety of devices (beamsplitters and
waveguide bends), we showcase the generality of our approach. With an area
footprint smaller than ${\lambda_0}^2$, our designs are amongst the smallest
reported to-date. Our method combines machine learning with digital
metamaterials to enable ultra-compact, manufacturable devices, which could
power a new "Photonics Moore's Law."
