arXiv subjects

Brian Edwards

Publications and source records attributed to Brian Edwards.

11 recordsLinked to original sources

arXiv2026

A Scalable All-to-All Reconfigurable Ising Solver Using Pulsed Time-Division Multiplexing

Physics-based computing platforms, such as those based on the Ising model, are an important pillar of future hardware systems built for the artificial intelligence (AI) era. Such platforms show promise for solving nondeterministic polynomial (NP) time problems that are difficult for traditional processing units to solve efficiently as problem size grows. Here, we present a scalable optoelectronic Ising machine architecture, demonstrated with 64 all-to-all connected spins using pulsed time-division multiplexing. The 65 nm CMOS Ising chip integrates the coupling and nonlinear mechanisms in an active area of 3.1 mm2, eliminating the need for benchtop equipment within the loop. The feedback loop of the Ising machine is closed using a compact high-bandwidth, low-loss optical fiber, seamlessly combining optical scalability with the ultradense reconfigurability of integrated electronics. The chip operates at 1 GHz with 4-bit coupling weights and is benchmarked with NP-complete Boolean satisfiability problems consisting of three literals (3-SAT) and clause-to-variable ratios of 32/32, 40/24, and 48/16. Nanosecond annealing times represent at least a three order-of-magnitude improvement over previously reported all-to-all connected works. Time and energy to solutions for 100% 3-SAT clause accuracy are as low as 7.4 us and 2.9 uJ, respectively, achieving more than an order-of-magnitude decrease in time and energy to solution compared to the state of the art. All-to-all connection is demonstrated using MaxCut problems with 100% graph densities. The chip's ability to effectively solve 2-, 3-SAT, and MaxCut problems highlights its reconfigurability and versatility. Furthermore, combining mature CMOS integration with scalable photonic links allows for significant reduction in computation time and energy, addressing the pressing requirements of AI and future hyperscale datacenters.

physics.optics↗

arXiv2025

A Kerr soliton Ising machine for combinatorial optimization problems

The growing challenges of scaling digital computing motivate new approaches, especially through the dynamical evolution of physical systems that mimic neural networks and combinatorial optimization problems. While light is a hyper efficient information carrier, intrinsically weak light interactions make direct information processing difficult to implement. Recently, specialized nonlinear photonics have opened new controls over light fields with extraordinary bandwidth, coherence, and the emergence of strong interactions among nonlinear eigenstates like solitons. We harness an ensemble of hundreds of Kerr-nonlinear microresonator solitons and implement an analog feedback network to create an Ising machine with fully programmable all-to-all interactions. By increasing the feedback for self, on-diagonal interactions, each soliton exhibits a universal spin-like bifurcation. Using this palette of interactions amongst the entire soliton ensemble, we encode the Ising machine to solve the benchmark Boolean satisfiability problem (SAT). The combination of uniform soliton interactions and the compatibility of our Ising machine with high-speed data interconnects enables rapid and precise solutions of complex SAT problems. Indeed, the soliton properties bound the tradeoff of optical power and time use by the machine at approximately 10 mW and 1 $\mu$s for a single feedback step. We performed >10,000 trials on more than 100 randomly generated SAT instances to evaluate the Ising machine, demonstrating the potential to exceed the performance of benchmark digital SAT solvers. Our work highlights the convergence of optical nonlinearity, ultralow loss photonics, and optoelectronic circuits, which can be combined for a wide range of computation-acceleration tasks.

physics.optics↗

arXiv2023

Holding and amplifying electromagnetic waves with temporal non-Foster metastructures

We introduce a mechanism that can both hold and amplify electromagnetic waves by rapidly changing the permittivity of the medium during the wave travel from a positive to a dispersionless (i.e. non-Foster) negative value and then back again. The underlying physics behind this phenomenon is theoretically explored by considering a plane wave in an unbounded medium. Interestingly, we show that a rapid positive-to-negative temporal change of {\epsilon}(t) causes the propagation of the wave to stop (observed by a frozen phase in time) while the amplitude of the frozen field exponentially grows. Stepping the permittivity back to the original (or a new) positive value will cause the wave to thaw and resume propagation with the original (or the new) frequency, respectively. We numerically study the case of dipole radiation in such time-varying non-Foster structures. As a possible implementation, we propose a parallel plate waveguide platform loaded with time-dependent media emulating parallel lumped non-Foster negative capacitors. Such non-Foster time-varying structures may open new venues in controlling and manipulating wave-matter interaction.

physics.optics↗

arXiv2023

Inverse-designed low-index-contrast structures on silicon photonics platform for vector-matrix multiplication

Inverse-designed Silicon photonic metastructures offer an efficient platform to perform analog computations with electromagnetic waves. However, due to computational difficulties, scaling up these metastructures to handle a large number of data channels is not trivial. Furthermore, a typical inverse-design procedure utilizes a small computational domain and therefore tends to employ resonant features to achieve its objectives. This results in structures that are narrow-bandwidth and highly sensitive to fabrication errors. Here, we employ a 2D inverse-design method based on the effective index approximation with a low-index contrast constraint. This results in compact amorphous lens systems which are generally feed-forward and low-resonance. We designed and experimentally demonstrated a vector-matrix product for a 2 x 2 and a 3 x 3 matrix. We also designed a 10 x 10 matrix using the proposed 2D computational method. These examples demonstrate that these techniques have the potential to enable larger-scale wave-based analog computing platforms.

physics.optics↗

arXiv2022

Programmable wave-based analog computing machine: a metastructure that designs metastructures

The ability to perform mathematical computations using metastructures is an emergent paradigm that carries the potential of wave-based analog computing to the realm of near-speed-of-light, low-loss, compact devices. We theoretically introduce and experimentally verify the concept of a reconfigurable metastructure that performs analog complex mathematical computations using electromagnetic waves. Reconfigurable, RF-based components endow our device with the ability to perform stationary and non-stationary iterative algorithms. After demonstrating matrix inversion (stationary problem), we use the machine to tackle two major non-stationary problems: root finding with Newton's method and inverse design (constrained optimization) via the Lagrange multiplier method. The platform enables possible avenues for wave-based, analog computations for general linear algebraic problems and beyond in compact, ultrafast, and parallelized ways.

physics.app-ph↗

arXiv2022

Mathematical Operations and Equation Solving with Reconfigurable Metadevices

Performing analog computations with metastructures is an emerging wave-based paradigm for solving mathematical problems. For such devices, one major challenge is their reconfigurability, especially without the need for a priori mathematical computations or computationally-intensive optimization. Their equation-solving capabilities are applied only to matrices with special spectral (eigenvalue) distribution. Here we report the theory and design of wave-based metastructures using tunable elements capable of solving integral/differential equations in a fully-reconfigurable fashion. We consider two architectures: the Miller architecture, which requires the singular-value decomposition, and an alternative intuitive direct-complex-matrix (DCM) architecture introduced here, which does not require a priori mathematical decomposition. As examples, we demonstrate, using system-level simulation tools, the solutions of integral and differential equations. We then expand the matrix inverting capabilities of both architectures toward evaluating the generalized Moore-Penrose matrix inversion. Therefore, we provide evidence that metadevices can implement generalized matrix inversions and act as the basis for the gradient descent method for solutions to a wide variety of problems. Finally, a general upper bound of the solution convergence time reveals the rich potential that such metadevices can offer for stationary iterative schemes.

physics.app-ph↗

arXiv2022

Solving integral equations in free-space with inverse-designed ultrathin optical metagratings

As standard microelectronic technology approaches fundamental limitations in speed and power consumption, novel computing strategies are strongly needed. Analog optical computing enables processing large amounts of data at a negligible energy cost and high speeds. Based on these principles, ultrathin optical metasurfaces have been recently explored to process large images in real-time, in particular for edge detection. By incorporating feedback, it has also been recently shown that metamaterials can be tailored to solve complex mathematical problems in the analog domain, although these efforts have so far been limited to guided-wave systems and bulky setups. Here, we present an ultrathin Si metasurface-based platform for analog computing that is able to solve Fredholm integral equations of the second kind using free-space visible radiation. A Si-based metagrating was inverse-designed to implement the scattering matrix synthesizing a prescribed Kernel corresponding to the mathematical problem of interest. Next, a semi-transparent mirror was incorporated into the sample to provide adequate feedback and thus perform the required Neumann series, solving the corresponding equation in the analog domain at the speed of light. Visible wavelength operation enables a highly compact, ultrathin device that can be interrogated from free-space, implying high processing speeds and the possibility of on-chip integration.

physics.optics↗

arXiv2020

A single inverse-designed photonic structure that performs parallel computing

In the search for improved computational capabilities, conventional microelectronic computers are facing various problems arising from the miniaturization and concentration of active electronics devices (1-2). Therefore, researchers have been exploring several paths for the next generation of computing platforms, which could exploit various physical phenomena for solving mathematical problems at higher speeds and larger capacities. Among others, physical systems described by waves, such as photonic and quantum devices, have been utilized to compute the solution of mathematical problems (1-18). However, previous devices have not fully exploited the linearity of the wave equation, which as we show here, allows for the simultaneous parallel solution of several independent mathematical problems within the same device. In this Letter, we demonstrate, theoretically and experimentally, that a transmissive cavity filled with a judiciously tailored dielectric distribution and embedded in a multi-frequency feedback loop can calculate the solutions of an arbitrary number of mathematical problems simultaneously. We design, build, and test a computing structure at microwave frequencies that solves two independent integral equations with any two arbitrary inputs. We offer another design that can invert four arbitrary 5x5 matrices, confirming its functionality with numerical simulations. We believe our results presented here can provide "coincident computing" and pave the way for the design of low-power, ultrafast, parallel photonic analog computing devices for sensing and signal processing in embedded computing applications.

physics.optics↗

arXiv2009

Nonlinear Control of Tunneling Through an Epsilon-Near-Zero Channel

The epsilon-near-zero (ENZ) tunneling phenomenon allows full transmission of waves through a narrow channel even in the presence of a strong geometric mismatch. Here we experimentally demonstrate nonlinear control of the ENZ tunneling by an external field, as well as self-modulation of the transmission resonance due to the incident wave. Using a waveguide section near cut-off frequency as the ENZ system, we introduce a diode with tunable and nonlinear capacitance to demonstrate both of these effects. Our results confirm earlier theoretical ideas on using an ENZ channel for dielectric sensing, and their potential applications for tunable slow-light structures.

physics.optics↗

arXiv2008

Reflectionless Sharp Bends and Corners in Waveguides Using Epsilon-Near-Zero Effects

Following our recent theoretical and experimental results that show how zero-permittivity metamaterials may provide anomalous tunneling and energy squeezing through ultranarrow waveguide channels, here we report an experimental investigation of the bending features relative to this counterintuitive resonant effect. We generate the required effectively-zero permittivity using a waveguide operating at the cut-off of its dominant mode, and we show how sharp and narrow bends may be inserted within the propagation channel without causing any sensible reflection or loss.

cond-mat.mtrl-sci↗

arXiv2006

Measuring Bullet Velocity with a PC Soundcard

This article describes a simple method for using a PC soundcard to accurately measure bullet velocity. The method involves placing the microphone within a foot of the muzzle and firing at a steel target between 50 and 100 yards away. The time of flight for the bullet is simply the recorded time between muzzle blast and sound of the bullet hitting the target minus the time it takes the sound to return from the target to the microphone. The average bullet velocity is simply the distance from the muzzle to the target divided by the time of flight of the bullet. This method can also be applied to measurement of paintball velocities.

physics.ed-ph↗