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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.

Recently, deep reinforcement learning (DRL) for metasurface design has received increased attention for its excellent decision-making ability in complex problems. However, time-consuming numerical simulation has hindered the adoption of DRL-based design method. Here we apply the Deep learning-based virtual Environment Proximal Policy Optimization (DE-PPO) method to design the 3D chiral plasmonic metasurfaces for flexible targets and model the metasurface design process as a Markov decision process to help the training. A well trained DRL agent designs chiral metasurfaces that exhibit the optimal absolute circular dichroism value (typically, ∼ 0.4) at various target wavelengths such as 930 nm, 1000 nm, 1035 nm, and 1100 nm with great time efficiency. Besides, the training process of the PPO agent is exceptionally fast with the help of the deep neural network (DNN) auxiliary virtual environment. Also, this method changes all variable parameters of nanostructures simultaneously, reducing the size of the action vector and thus the output size of the DNN. Our proposed approach could find applications in efficient and intelligent design of nanophotonic devices.