Fig. 2 | Scientific Reports

Fig. 2

From: Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning methodology

Fig. 2

(a) Structure of the DNN-CP with 8-layer active region superlattice input features and five different output features, (b) structure of the DNN-TP based on DL with 24-layer total QCL structure and five different output feature, (c) structure of the DNN-TP based on the GTL where the pretrained weights of the DNN-CP are transferred, (d) structure of the DNN-TP based on our proposed PBTL of which the first hidden layer is divided into two parts that can process thickness of active and injector layers, respectively.

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