In recent years, optimizing the design of photonic structures has become a key area of nanophotonic research. Traditional methods like shape optimization and topology optimization each have their advantages, but also limitations. Shape optimization is fast and easy to manufacture but struggles with achieving the best possible performance, especially for complex designs. Topology optimization, on the other hand, can create more innovative designs but often results in structures that are hard to fabricate. In this work, we propose a new method that combines the best features of both shape and topology optimization. We apply this method to design photonic devices like gratings, which are used in many optical applications. To make the process more efficient, we construct backpropagation in nanophotonic structures inspired by neural networks. This allows us to quickly calculate the gradients of the parameters, speeding up the optimization process. We design a highly efficient blazed grating and a polarizing beam splitter by using our method. Better results are achieved than the previous, including finding the structure with high first-order performance over a broader spectrum and improving the performance of the polarizing beam splitter from 96% to 98.8%. By reducing optimization time from over 11,000 hours to just 38 hours, our method opens up new possibilities for faster and more efficient optical design.