| AVS 65th International Symposium & Exhibition | |
| Electronic Materials and Photonics Division | Tuesday Sessions |
| Session EM+2D+AN+MI+MP+NS-TuA |
| Session: | Solar/Energy Harvesting and Quantum Materials and Applications |
| Presenter: | Jelena Vuckovic, Stanford University |
| Correspondent: | Click to Email |
Photonics has numerous applications ranging from optical interconnects, classical and quantum computing, to sensing (such as LIDAR and AR), and imaging. However, the state of the art photonics is bulky, inefficient, sensitive to environment, lossy, and its performance is severely degraded in real-world environment as opposed to ideal laboratory conditions, which has prevented from using it in many practical applications. Therefore, it is clear that new approaches for implementing photonics are crucial.
We have recently developed a computational approach to inverse-design photonics based on desired performance, with fabrication constraints and structure robustness incorporated in design process [1,2]. Our approach performs physics guided search through the full parameter space until the optimal solution is reached. Resulting device designs are non-intuitive (see Figure), but are fabricable using standard techniques, resistant to temperature variations of hundreds of degrees, typical fabrication errors, and they outperform state of the art counterparts by many orders of magnitude in footprint, efficiency and stability. This is completely different from conventional approach to design photonics, which is almost always performed by brute-force or intuition-guided tuning of a few parameters of known structures, until satisfactory performance is achieved, and which almost always leads to sub-optimal designs.
Apart from integrated photonics, our approach is also applicable to any other optical and quantum optical devices and systems. In recent years, color centers in diamond and silicon carbide (SiC) have emerged as a possible platform for implementation of quantum circuits [3,4]. We demonstrate how such quantum hardware can also be optimized to be robust, efficient, and scalable.
References
[1] A. Piggott et al, Nature Photonics 9, 374–377 (2015)]
[2] L. Su et al, ACS Photonics ASAP (2018)
[3] J.L. Zhang et al, Nano Letters 18 (2), 1360-1365 (2018)
[4] M. Radulaski et al, Nano Letters 17 (3), 1782-1786 (2017)