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A promising type of Reconfigurable Intelligent Surface (RIS) employs tunable control of its varactors using biasing transmission lines below the RIS reflecting elements. Biasing standing waves (BSWs) are excited by a time-periodic signal and sampled at each RIS element to create a desired biasing voltage and control the reflection coefficients of the elements. A simple rectifier can be used to sample the voltages and capture the peaks of the BSWs over time. Like other types of RIS, attempting to model and accurately configure a wave-controlled RIS is extremely challenging due to factors such as device non-linearities, frequency dependence, element coupling, etc., and thus significant differences will arise between the actual and assumed performance. An alternative approach to solving this problem is data-driven: Using training data obtained by sampling the reflected radiation pattern of the RIS for a set of BSWs, a neural network (NN) is designed to create an input-output map between the BSW amplitudes and the resulting sampled radiation pattern. This is the approach discussed in this paper. In the proposed approach, the NN is optimized using a Genetic Algorithm (GA) to minimize the error between the estimated and measured radiation patterns. The BSW amplitudes are then designed via Simulated Annealing (SA) to optimize a signal-to-leakage-plus-noise ratio measure by iteratively forward-propagating the BSW amplitudes through the NN and using its output as feedback to determine convergence. The resulting optimal solutions are stored in a lookup table to be used both as settings to instantly configure the RIS and as a basis for determining more complex radiation patterns. 

An innovative method has developed recently for biasing the varactors of a reconfigurable intelligent surface (RIS) by utilizing resonant standing waves on the “biasing transmission line (TL)” [E. Ayanoglu, F. Capolino, and A. L. Swindlehurst, “Wave-controlled metasurface-based reconfigurable intelligent surfaces,” IEEE Wireless Communications, vol. 29, no. 4, pp. 86-92,2022] located beneath the reflective surface. Using this approach, each RIS element does not require separate external biasing. For estimating the RIS reflection properties controlled by varactors, we analyze a planar array with phase gradient in one direction, of side length L, of reconfigurable elements. We employ the analytical model for predicting the reflection coefficients of the unit cells presented in [D. Hanna, M. Saavedra-Melo, F. Shan, and F. Capolino, “A versatile polynomial model for reflection by a reflective intelligent surface with varactors,” IEEE AP-S/URSI, 2022] and investigate how the standing wave biasing approach compares with the traditional way to generate field patterns of the reflected wave. 

A Reconfigurable Intelligent Surface (RIS) consists of many small reflective elements whose reflection properties can be adjusted to change the wireless propagation environment. Envisioned implementations require that each RIS element be connected to a controller, and as the number of RIS elements on a surface may be on the order of hundreds or more, the number of required electrical connectors creates a difficult wiring problem. A potential solution to this problem was previously proposed by the authors in which “biasing transmission lines” carrying standing waves are sampled at each RIS location to produce the desired bias voltage for each RIS element. This paper presents models for the RIS elements that account for mutual coupling and realistic varactor characteristics, as well as circuit models for sampling the transmission line to generate the RIS control signals. The paper investigates two techniques for conversion of the transmission line standing wave voltage to the varactor bias voltage, namely an envelope detector and a sample-and-hold circuit. The paper also develops a modal decomposition approach for generating standing waves that are able to generate beams and nulls in the resulting RIS radiation pattern that maximize either the Signal-to-Noise Ratio (SNR) or the Signal-to-Leakage-plus-Noise Ratio (SLNR). The paper provides five algorithms, two for the case of the envelope detector, one for the sample-and-hold circuit, one for pursuing the global minimum for both circuits, and one for simultaneous beam and null steering. Extensive simulation results show that while the envelope detector is simpler to implement, the sample-and-hold circuit has substantially better performance and runs in substantially less time. In addition, the wave-controlled RIS is able to generate strong beams and deep nulls in desired directions. This is in contrast with the case of arbitrary control of each varactor element and idealized RIS models. 

A novel method for biasing the varactors of a reconfigurable intelligent surface (RIS) by using resonant standing waves on the biasing transmission line (TL) at a layer below the RF reflective surface to eliminate the need to bring external bias for each element of the RIS is described. We use an analytical model of the RIS to compare the field pattern of the reflected wave by (i) considering the ideal case, (ii) the case where reflection accounts for the varactor's model, and (iii) the case as in (ii) but where the biasing voltage distribution is constructed by using the wave control (i.e., standing waves). 

The physics of exceptional points leads to very high sensitivity because the perturbation of an exceptionally degenerate state is highly sensitive to a system’s perturbation. This property is indeed not shared with nondegenerate systems, and it relies in the fractional power expansion (Puiseux series) describing the perturbation of eigenvalues and eigenvectors. We discuss how this property is met in systems made of coupled resonators and with coupled modes in waveguides, whose eigenvalues are the resonant frequencies and the wavenumbers, respectively. We will also discuss the experimental implementation of this principle in unstable nonlinear systems to build extremely sensitive saturated oscillators. 

We design a three-way silicon optical waveguide with the Bloch dispersion relation supporting a stationary inflection point (SIP). The SIP is a third order exceptional point of degeneracy (EPD) where three Bloch modes coalesce forming the frozen mode with greatly enhanced amplitude. The proposed design consists of a coupled resonators optical waveguide (CROW) coupled to a parallel straight waveguide. At any given frequency, this structure supports three pairs of reciprocal Bloch eigenmodes, propagating and/or evanescent. In addition to full-wave simulations, we also employ a so-called “hybrid model” that uses transfer matrices obtained from full-wave simulations of sub-blocks of the unit cell. This allows us to account for radiation losses and enables a design procedure based on minimizing the eigenmodes’ coalescence parameter. The proposed finite-length CROW displays almost unitary transfer function at the SIP resonance, implying a nearly perfect conversion of the input light into the frozen mode. The group delay and the effective quality factor at the SIP resonance show an $N^3$ scaling, where N is the number of unit cells in the cavity. The frozen mode in the CROW can be utilized in various applications like sensors, lasers and optical delay lines. 

We will discuss two kinds of exceptional points of degeneracy in waveguides and their respective application in lasers. Such exceptional points occur in waveguides with balanced loss and gain (e.g., PT symmetry), and in waveguides without loss and gain (e.g., periodic Si waveguides). Waveguides with such exceptional points have a strong degeneracy of their wavenumbers and polarization states that enables specific wave physics, only found in these degenerate systems. We will discuss advantages and disadvantages of both concepts to conceive laser regimes, related to high power, high spectral purity, high efficiency, etc, and show some realistic designs involving Si ridge waveguides.