Optimized Multi-layer Interference for Color-tuned and Transparent Colloidal Quantum Dot Solar Cells

ORAL

Abstract

Due to their band gap tunability and near infrared responsivity, colloidal quantum dots (CQDs) are promising active materials for mitigating absorption and photocurrent losses in color-tuned and transparent solar cells. Typical CQD solar cells are multi-layered structures comprised of optically thin layers that are optimized for their electrical properties. In this work, we develop an optimization algorithm to explore the multi-dimensional thickness space that controls multi-layer optical interference in CQD solar cells to simultaneously optimize devices for their electrical and optical performance. The tradeoffs between attainable color or transparency and available photocurrent are quantified, and the effects of non-ideal interference on apparent color are taken into account. Using our new designs, we fabricated blue, green, yellow and red CQD solar cells with photocurrents ranging from 10 mA/cm2 to 15 mA/cm2 and semitransparent devices with visible transparencies ranging from 27% to 32%. In addition, our optimization method can be adapted for custom-design of multi-layer structured optoelectronic devices with arbitrary spectral profiles.

Presenters

  • Botong Qiu

    Electrical and Computer Engineering, Johns Hopkins University

Authors

  • Botong Qiu

    Electrical and Computer Engineering, Johns Hopkins University

  • Ebuka Arinze

    Electrical and Computer Engineering, Johns Hopkins University

  • Nathan Palmquist

    Electrical and Computer Engineering, Johns Hopkins University

  • Yan Cheng

    Electrical and Computer Engineering, Johns Hopkins University

  • Yida Lin

    Electrical and Computer Engineering, Johns Hopkins University

  • Gabrielle Nyirjesy

    Material Science and Engineering, Johns Hopkins University

  • Gary Qian

    Electrical and Computer Engineering, Johns Hopkins University

  • Susanna Thon

    Electrical and Computer Engineering, Johns Hopkins University