The ability to tune photonic crystals (PCs) to spectrally select wavelengths of interest has great merit for many optoelectronic devices, such as multijunction solar cells. Here, we build off of pre-existing research on how material absorption effects the properties of photonic bands by integrating a wavelength-dependent permittivity model of lead-sulfide (PbS) thin-film quantum dots (QDs). We generated a periodic PbS PC slab that optimizes absorption in the infrared (IR) with varying degrees of weights on the importance of this absorption, and we demonstrated consistency with basic understandings of spectral interference as well as observed the interesting phenomenon of natural groupings of structures based on the value of the weights. Then, we investigated the effect of non-symmetrical unit cell configurations on the spectral profile. We discovered that the spectra of these non-geometric configurations have very rich spectral features indicating set of design parameters for intricate spectral tuning and selectivity in absorbing films.
Future work will involve utilizing deep learning (DL) to produce fast methods for predicting the spectra of the given PC and to reverse engineer the PC from a specified spectra.

Team Members

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Project Mentors, Sponsors, and Partners

• Susanna Thon

• Sreyas Chintapalli

• Yida Lin

• Susanna M. Thon

Course Faculty

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Project Links

Additional Project Information