Abstract
The active core (AC) of a quantum cascade laser (QCL) is a superlattice with hundreds of layers, which embodies stages consisting of 10-12 quantum wells repeating 30 to 50 times. Optimization of these layer thicknesses and compositions yields the desired device performance metrics for a given structure. As part of the device-design processfor these structures, human intervention is needed at multiple steps from identifying relevant wavefunctions to analyzing device-performance metrics. This is a time-consuming process ranging from weeks to months with little to no guarantee of success. We aim to replace this human intervention aspect of the AC design by training deep neural networks (DNNs) and implement an inverse design approach. To train these DNNs, datasets of QCL-AC structure and corresponding performance metrics are used to develop instinctive connections between them. After training is finished, we expect this network to achieve an AC design based on input device performance metrics in a significantly shorter timeframe.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
