Deep learning for the design of 3D chiral plasmonic metasurfaces

Chiral plasmonic metasurfaces are promising for enlarging the chiral signals of biomolecules and improving the sensitivity of bio-sensing. However, the design process of the chiral plasmonic nanostructures is time consuming. Deep learning has been playing a key role in the design of photonic devices with high time efficiency and good design performance. This paper proposes a deep neural network (DNN) to achieve forward prediction and inverse design for 3D chiral plasmonic metasurfaces, and further improve the training speed and performance by the transfer learning method. Once the DNNs are trained using a part of the sampled data from the parameter space, the circular dichroism (CD) spectra can be predicted within the time on milliseconds (about 3.9 ms for forward network and 5.6 ms for inverse network) with high prediction accuracy. The inverse design was optimized by taking more spectral information into account and extracting the critical features using the one-dimensional convolutional kernel. The aforementioned trained network for one handedness can accelerate the training speed and improve performance with small datasets for the opposite handedness via the transfer learning method. The proposed approach is instructive in the design process of chiral plasmonic metasurfaces and could find applications in exploring versatile complex nanophotonic devices efficiently.