Light harvesting coating design accelerated by deep learning for semi-transparent polymer solar cells

摘要

The reduction in optical loss in polymer solar cells (PSCs) plays a crucial role in the development of high-performance PSCs devices. Especially for the semi-transparent PSCs, high reflective transparent electrodes lead to low energy utilization. Optical multi-layer coating is proven to be an effective approach to reduce the reflection and transmission loss. In this work, a double-sided PSCs device coating strategy was used to reduce the device optical loss. Optical coating design on a multi-layer PSCs device is far more complex. The dispersion and thick-ness of each layer both have an impact on the optical property. Meanwhile, the illuminance spectrum is based on the solar AM 1.5 spectrum rather than a common-used standard illuminance CIE-E spectrum. It brings many difficulties to the optical design, and the global optimiza-tion is generally time-consuming. To fast solve the optimization problem in optical design of the multi-layer coating for PSCs, we combine deep learning (DL) method with hybrid optimization algorithms. By designing a multi-layer device structure to achieve the highest light har-vesting with tandem simplex simulated annealing and assisted simplex simulated annealing, we show unambiguously that DL is a powerful tool to minimize the computation cost and maximize the design efficiency for optical multi-layer design. The optical loss of the semi-transparent device is reduced from 52.71% to 27.95%, and the simulation time is reduced by a factor of 276 compared with standard simplex simulated annealing. This provides an efficient optical design strategy in multi-layer coating design for PSCs to achieve desired optical performance.