We demonstrate resonant tunneling diodes, embedded in double metal cavities, strongly coupled to the cavity field, while maintaining their electronic properties. We measure the polariton dispersion and find a relative vacuum Rabi splitting of 11%, which explicitly qualifies for the strong-coupling regime. Additionally, we show that electronic transport has a significant influence on the polaritons by modulating the coupling strength. The merge between electronic transport and polaritonic physics in our devices opens up different perspectives of cavity quantum electro-dynamics and integrated photonics.

Artificial neural networks are capable of fitting highly non-linear and complex systems. Such complicated systems can be found everywhere in nature, including the non-linear interaction between optical modes in laser resonators. In this work, we demonstrate artificial neural networks trained to model these complex interactions in the cavity of a Quantum Cascade Random Laser. The neural networks are able to predict modulation schemes for desired laser spectra in real-time. This radically novel approach makes it possible to adapt spectra to individual requirements without the need for lengthy and costly simulation and fabrication iterations.

We demonstrate an optical machine learning method in the terahertz domain, which allows the recognition of objects within a single measurement. As many materials are transparent in the terahertz spectral region, objects hidden within such materials can be identified. In contrast to typical object recognition methods, our method only requires a single pixel detector instead of a focal plane array. The core of the calculation is performed by a quantum cascade laser generated terahertz beam, which is spatially modulated at a near-infrared encoded silicon wafer. We show that this method is robust against displacements of the objects and noise. Additionally, the method is flexible and, due to the optically performed recognition task, inherently fast.

No abstract

No abstract