Search for: All records

The spectral sensitivity near exceptional points (EPs) has been recently explored as an avenue for building sensors with enhanced sensitivity. However, to date, it is not clear whether this class of sensors does indeed outperform traditional sensors in terms of signal-to-noise ratio. In this work, we investigate the spectral sensitivity associated with EPs under a different lens and propose to utilize it as a resource for hardware security. In particular, we introduce a physically unclonable function (PUF) based on analogue electronic circuits that benefit from the drastic eigenvalues bifurcation near a divergent exceptional point to enhance the stochastic entropy caused by inherent parameter fluctuations in electronic components. This in turn results in a perfect entropy source for the generation of encryption keys encoded in analog electrical signals. This lightweight and robust analog-PUF structure may lead to a variety of unforeseen securities and anti-counterfeiting applications in radio-frequency fingerprinting and wireless communications.

 

Parity-time-reciprocal scaling (PTX)-symmetry has been recently proposed to tailor the resonance linewidth and gain threshold of non-Hermitian systems with new exhilarating applications, such as coherent perfect absorber-laser (CPAL) and exceptional point (EP)-based devices. Here, we put forward a nearly-lossless, low-index metachannel formed byPTX-symmetric metasurfaces operating at the CPAL point, supporting the undamped weakly-guided fast wave (leaky mode) and thus achieving ultradirective leaky-wave radiation. Moreover, this structure allows for a reconfigurable and tunable radiation angle as well as beamwidth determined by the reciprocally scaled gain-loss parameter. We envision that the proposedPTX-symmetric metasurfaces will shed light on the design of antennas and emitters with ultrahigh directionality, as well as emerging applications enabled by extreme material properties, such as epsilon-near-zero (ENZ) and beyond.

 

An all-dielectric photonic metastructure is investigated for application as a quantum algorithm emulator (QAE) in the terahertz frequency regime; specifically, we show implementation of the Deustsh-Josza algorithm. The design for the QAE consists of a gradient-index (GRIN) lens as the Fourier transform subblock and patterned silicon as the oracle subblock. First, we detail optimization of the GRIN lens through numerical analysis. Then, we employed inverse design through a machine learning approach to further optimize the structural geometry. Through this optimization, we enhance the interaction of the incident light with the metamaterial via spectral improvements of the outgoing wave. 

A metastructure-based on a graded-index lens is presented for the terahertz regime as an emulator for quantum algorithms. Further optimization is presented by applying machine learning in an inverse design strategy.