arXiv subjects

David A. B. Miller

Publications and source records attributed to David A. B. Miller.

At least 37 records · Page 2Linked to original sources

arXiv2023

Scalable Low-latency Optical Phase Sensor Array

Optical phase measurement is critical for many applications and traditional approaches often suffer from mechanical instability, temporal latency, and computational complexity. In this paper, we describe compact phase sensor arrays based on integrated photonics, which enable accurate and scalable reference-free phase sensing in a few measurement steps. This is achieved by connecting multiple two-port phase sensors into a graph to measure relative phases between neighboring and distant spatial locations. We propose an efficient post-processing algorithm, as well as circuit design rules to reduce random and biased error accumulations. We demonstrate the effectiveness of our system in both simulations and experiments with photonic integrated circuits. The proposed system measures the optical phase directly without the need for external references or spatial light modulators, thus providing significant benefits for applications including microscope imaging and optical phased arrays.

physics.optics↗

arXiv2022

Scalable and self-correcting photonic computation using balanced photonic binary tree cascades

Programmable unitary photonic networks that interfere hundreds of modes are emerging as a key technology in energy-efficient sensing, machine learning, cryptography, and linear optical quantum computing applications. In this work, we establish a theoretical framework to quantify error tolerance and scalability in a more general class of "binary tree cascade'' programmable photonic networks that accept up to tens of thousands of discrete input modes $N$. To justify this scalability claim, we derive error tolerance and configuration time that scale with $\log_2 N$ for balanced trees versus $N$ in unbalanced trees, despite the same number of total components. Specifically, we use second-order perturbation theory to compute phase sensitivity in each waveguide of balanced and unbalanced networks, and we compute the statistics of the sensitivity given random input vectors. We also evaluate such networks after they self-correct, or self-configure, themselves for errors in the circuit due to fabrication error and environmental drift. Our findings have important implications for scaling photonic circuits to much larger circuit sizes; this scaling is particularly critical for applications such as principal component analysis and fast Fourier transforms, which are important algorithms for machine learning and signal processing.

physics.optics↗

arXiv2022

Experimental evaluation of digitally-verifiable photonic computing for blockchain and cryptocurrency

As blockchain technology and cryptocurrency become increasingly mainstream, ever-increasing energy costs required to maintain the computational power running these decentralized platforms create a market for more energy-efficient hardware. Photonic cryptographic hash functions, which use photonic integrated circuits to accelerate computation, promise energy efficiency for verifying transactions and mining in a cryptonetwork. Like many analog computing approaches, however, current proposals for photonic cryptographic hash functions that promise similar security guarantees as Bitcoin are susceptible to systematic error, so multiple devices may not reach a consensus on computation despite high numerical precision (associated with low photodetector noise). In this paper, we theoretically and experimentally demonstrate that a more general family of robust discrete analog cryptographic hash functions, which we introduce as LightHash, leverages integer matrix-vector operations on photonic mesh networks of interferometers. The difficulty of LightHash can be adjusted to be sufficiently tolerant to systematic error (calibration error, loss error, coupling error, and phase error) and preserve inherent security guarantees present in the Bitcoin protocol. Finally, going beyond our proof-of-concept, we define a ``photonic advantage'' criterion and justify how recent developments in CMOS optoelectronics (including analog-digital conversion) provably achieve such advantage for robust and digitally-verifiable photonic computing and ultimately generate a new market for decentralized photonic technology.

cs.ET↗

arXiv2022

Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks

Neural networks are widely deployed models across many scientific disciplines and commercial endeavors ranging from edge computing and sensing to large-scale signal processing in data centers. The most efficient and well-entrenched method to train such networks is backpropagation, or reverse-mode automatic differentiation. To counter an exponentially increasing energy budget in the artificial intelligence sector, there has been recent interest in analog implementations of neural networks, specifically nanophotonic neural networks for which no analog backpropagation demonstration exists. We design mass-manufacturable silicon photonic neural networks that alternately cascade our custom designed "photonic mesh" accelerator with digitally implemented nonlinearities. These reconfigurable photonic meshes program computationally intensive arbitrary matrix multiplication by setting physical voltages that tune the interference of optically encoded input data propagating through integrated Mach-Zehnder interferometer networks. Here, using our packaged photonic chip, we demonstrate in situ backpropagation for the first time to solve classification tasks and evaluate a new protocol to keep the entire gradient measurement and update of physical device voltages in the analog domain, improving on past theoretical proposals. Our method is made possible by introducing three changes to typical photonic meshes: (1) measurements at optical "grating tap" monitors, (2) bidirectional optical signal propagation automated by fiber switch, and (3) universal generation and readout of optical amplitude and phase. After training, our classification achieves accuracies similar to digital equivalents even in presence of systematic error. Our findings suggest a new training paradigm for photonics-accelerated artificial intelligence based entirely on a physical analog of the popular backpropagation technique.

cs.ET↗

arXiv2022

Bounds on the Coupling Strengths of Communication Channels and Their Information Capacities

The concept of optimal communication channels shapes our understanding of wave-based communication. Its analysis, however, always pertains to specific communication-domain geometries, without a general theory of scaling laws or fundamental limits. In this article, we derive shape-independent bounds on the coupling strengths and information capacities of optimal communication channels for any two domains that can be separated by a spherical surface. Previous computational experiments have always observed rapid, exponential decay of coupling strengths, but our bounds predict a much slower, sub-exponential optimal decay, and specific source/receiver distributions that can achieve such performance. Our bounds show that domain sizes and configurations, and not domain shapes, are the keys to maximizing the number of non-trivial communication channels and total information capacities. Applicable to general wireless and optical communication systems, our bounds reveal fundamental limits to what is possible through engineering the communication domains of electromagnetic waves.

cs.IT↗

arXiv2022

Spatially resolving amplitude and phase of light with a reconfigurable photonic integrated circuit

Photonic integrated circuits (PICs) play a pivotal role in many applications. Particularly powerful are circuits based on meshes of reconfigurable Mach-Zehnder interferometers as they enable active processing of light. Various possibilities exist to get light into such circuits. Sampling an electromagnetic field distribution with a carefully designed free-space interface is one of them. Here, a reconfigurable PIC is used to optically sample and process free-space beams so as to implement a spatially resolving detector of amplitudes and phases. In order to perform measurements of this kind we develop and experimentally implement a versatile method for the calibration and operation of such integrated photonics based detectors. Our technique works in a wide parameter range, even when running the chip off the design wavelength. Amplitude, phase and polarization sensitive measurements are of enormous importance in modern science and technology, providing a vast range of applications for such detectors.

physics.optics↗

arXiv2021

Multibeam Free Space Optics Receiver Enabled by a Programmable Photonic Mesh

Free-space optics (FSO) is an attractive technology to meet the ever-growing demand for wireless bandwidth in next generation networks. To increase the spectral efficiency of FSO links, transmission over spatial division multiplexing (SDM) can be exploited, where orthogonal light beams have to be shaped according to suitable amplitude, phase, and polarization profiles. In this work, we show that a programmable photonic circuits, consisting of a silicon photonic mesh of tunable Mach-Zehnder Interferometers (MZIs) can be used as an adaptive multibeam receiver for a FSO communication link. The circuit can self-configure to simultaneously receive and separate, with negligible mutual crosstalk, signals carried by orthogonal FSO beams sharing the same wavelength and polarization. This feature is demonstrated on signal pairs either arriving at the receiver from orthogonal directions (direction-diversity) or being shaped according to different orthogonal spatial modes (mode-diversity), even in the presence of some mixing during propagation. The performance of programmable mesh as an adaptive multibeam receiver is assessed by means of data channel transmission at 10 Gbit/s a wavelength of 1550 nm, but the optical bandwidth of the receiver (>40 nm) allows its use at much higher data rates as well as in wavelength-division multiplexing SDM communication links.

physics.app-ph↗

arXiv2021

Inverse-designed multi-dimensional silicon photonic transmitters

Modern microelectronic processors have migrated towards parallel computing architectures with many-core processors. However, such expansion comes with diminishing returns exacted by the high cost of data movement between individual processors. The use of optical interconnects has burgeoned as a promising technology that can address the limits of this data transfer. While recent pushes to enhance optical communication have focused on developing wavelength-division multiplexing technology, this approach will eventually saturate the usable bandwidth, and new dimensions of data transfer will be paramount to fulfill the ever-growing need for speed. Here we demonstrate an integrated intra- and inter-chip multi-dimensional communication scheme enabled by photonic inverse design. Using inverse-designed mode-division multiplexers, we combine wavelength- and mode- multiplexing and send massively parallel data through nano-photonic waveguides and optical fibres. Crucially, as we take advantage of an orthogonal optical basis, our approach is inherently scalable to a multiplicative enhancement over the current state of the art.

physics.app-ph↗

arXiv2021

Automated manipulation of free space optical beams with integrated silicon photonic meshes

Free-space optics (FSO) offer promising solutions in wireless systems where many different devices need to communicate and interoperate, exchanging massive amounts of data as in communication and sensor networks, and in technologies for positioning and ranging. In these applications, control and manipulation of the light field is required for properly generating and correctly receiving FSO beams even in the presence of unpredictable objects and turbulence in the light path. In this work, we use a programmable mesh of Mach-Zehnder Interferometers (MZIs) to automatically control the complex field radiated and captured by an array of optical antennas. Several functionalities are demonstrated, including the generation of perfectly shaped beams with non-perfect optical antennas, the imaging of a desired field pattern through an obstacle or a diffusive medium, and the identification of an unknown obstacle inserted in the FSO path. Compared to conventional devices used for the manipulation of FSO beams, such as spatial light modulators (SLMs), our programmable circuit can self-configure through automated control strategies and can be integrated with other functionalities implemented onto the same photonic chip.

physics.optics↗

arXiv2020

Analyzing and generating multimode optical fields using self-configuring networks

Working with finite numbers of modes to describe, generate and detect optical fields can be both mathematically economical and physically useful. Such a modal basis can map directly to various applications in communications, sensing and processing. But, we need a way to generate and analyze such fields, including measurement and control of both the relative amplitudes and phases of the modal components. Here, we show first how to measure all those relative amplitudes and phases automatically and simultaneously. The method uses a self-configuring network of 2x2 blocks, such as integrated Mach-Zehnder interferometers, that can automatically align itself to the optical field by a sequence of simple one-parameter power minimizations when network elements, such as phase shifters, are adjusted. The optical field is then directly deduced from the resulting settings of those elements. We show how the entire network can be calibrated for such measurements, automatically and with just two light beams. Then, using the same calibration and running the mesh backwards, we can also controllably generate an arbitrary multimode field. Explicit algorithms and formulas are given for operating this system.

physics.optics↗

arXiv2020

Development of Quantum InterConnects for Next-Generation Information Technologies

Just as classical information technology rests on a foundation built of interconnected information-processing systems, quantum information technology (QIT) must do the same. A critical component of such systems is the interconnect, a device or process that allows transfer of information between disparate physical media, for example, semiconductor electronics, individual atoms, light pulses in optical fiber, or microwave fields. While interconnects have been well engineered for decades in the realm of classical information technology, quantum interconnects (QuICs) present special challenges, as they must allow the transfer of fragile quantum states between different physical parts or degrees of freedom of the system. The diversity of QIT platforms (superconducting, atomic, solid-state color center, optical, etc.) that will form a quantum internet poses additional challenges. As quantum systems scale to larger size, the quantum interconnect bottleneck is imminent, and is emerging as a grand challenge for QIT. For these reasons, it is the position of the community represented by participants of the NSF workshop on Quantum Interconnects that accelerating QuIC research is crucial for sustained development of a national quantum science and technology program. Given the diversity of QIT platforms, materials used, applications, and infrastructure required, a convergent research program including partnership between academia, industry and national laboratories is required. This document is a summary from a U.S. National Science Foundation supported workshop held on 31 October - 1 November 2019 in Alexandria, VA. Attendees were charged to identify the scientific and community needs, opportunities, and significant challenges for quantum interconnects over the next 2-5 years.

quant-ph↗

arXiv2019

Parallel fault-tolerant programming of an arbitrary feedforward photonic network

Reconfigurable photonic mesh networks of tunable beamsplitter nodes can linearly transform $N$-dimensional vectors representing input modal amplitudes of light for applications such as energy-efficient machine learning hardware, quantum information processing, and mode demultiplexing. Such photonic meshes are typically programmed and/or calibrated by tuning or characterizing each beam splitter one-by-one, which can be time-consuming and can limit scaling to larger meshes. Here we introduce a graph-topological approach that defines the general class of feedforward networks commonly used in such applications and identifies columns of non-interacting nodes that can be adjusted simultaneously. By virtue of this approach, we can calculate the necessary input vectors to program entire columns of nodes in parallel by simultaneously nullifying the power in one output of each node via optoelectronic feedback onto adjustable phase shifters or couplers. This parallel nullification approach is fault-tolerant to fabrication errors, requiring no prior knowledge or calibration of the node parameters, and can reduce the programming time by a factor of order $N$ to being proportional to the optical depth (or number of node columns in the device). As a demonstration, we simulate our programming protocol on a feedforward optical neural network model trained to classify handwritten digit images from the MNIST dataset with up to 98% validation accuracy.

cs.ET↗

arXiv2019

Matrix optimization on universal unitary photonic devices

Universal unitary photonic devices can apply arbitrary unitary transformations to a vector of input modes and provide a promising hardware platform for fast and energy-efficient machine learning using light. We simulate the gradient-based optimization of random unitary matrices on universal photonic devices composed of imperfect tunable interferometers. If device components are initialized uniform-randomly, the locally-interacting nature of the mesh components biases the optimization search space towards banded unitary matrices, limiting convergence to random unitary matrices. We detail a procedure for initializing the device by sampling from the distribution of random unitary matrices and show that this greatly improves convergence speed. We also explore mesh architecture improvements such as adding extra tunable beamsplitters or permuting waveguide layers to further improve the training speed and scalability of these devices.

eess.SP↗

arXiv2019

An introduction to functional analysis for science and engineering

This is a tutorial introduction to the functional analysis mathematics needed in many physical problems, such as in waves in continuous media. Functional analysis takes us beyond finite matrices, allowing us to work with infinite sets of continuous functions. It resolves important issues, such as whether, why and how we can practically reduce such problems to finite matrix approximations. It is, however, difficult to find a readable introduction that is efficient and comprehensible for scientists and engineers. Here, I have selected only the topics necessary for the most important results, but the argument is mathematically complete and self-contained. The article starts from sets and sequences of real numbers. It then develops spaces of vectors or functions, introducing the concepts of norms and metrics that allow us to consider how these can converge. Adding the inner product, it introduces Hilbert spaces, and the key forms of operators that map within or between such spaces. This leads to the concept of compact operators, which allows us to resolve many difficulties of working with infinite sets of vectors or functions. We then introduce Hilbert-Schmidt operators, which are compact operators encountered extensively in physical problems, such as those involving waves. Finally, it introduces the eigenfunctions for major classes of operators, and their powerful properties, and ends with singular-value decomposition of operators. This article is written in a style that is complementary to that of standard mathematical treatments; by relegating longer proofs to a separate section, I have attempted to retain a clear narrative flow and motivation in developing the mathematical structure. Hopefully, the result is useful to a broader readership who need to understand this mathematics, especially in physical science and engineering.

math.FA↗

arXiv2019

Waves, modes, communications and optics

Modes generally provide an economical description of waves, reducing complicated wave functions to finite numbers of mode amplitudes, as in propagating fiber modes and ideal laser beams. But finding a corresponding mode description for counting the best orthogonal channels for communicating between surfaces or volumes, or for optimally describing the inputs and outputs of a complicated optical system or wave scatterer, requires a different approach. The singular-value decomposition approach we describe here gives the necessary optimal source and receiver "communication modes" pairs and device or scatterer input and output "mode-converter basis function" pairs. These define the best communication or input/output channels, allowing precise counting and straightforward calculations. Here we introduce all the mathematics and physics of this approach, which works for acoustic, radio-frequency and optical waves, including full vector electromagnetic behavior, and is valid from nanophotonic scales to large systems. We show several general behaviors of communications modes, including various heuristic results. We also establish a new "M-gauge" for electromagnetism that clarifies the number of vector wave channels and allows a simple and general quantization. This approach also gives a new modal "M-coefficient" version of Einstein's A&B coefficient argument and revised versions of Kirchhoff's radiation laws. The article is written in a tutorial style to introduce the approach and its consequences.

physics.optics↗

arXiv2019

Experimental band structure spectroscopy along a synthetic dimension

In recent years there has been significant interest in the concepts of synthetic dimensions, where one couples the internal degrees of freedom of a particle to form higher-dimensional lattices in lower-dimensional physical structures. For these systems the concept of band structure along the synthetic dimension plays a central role in their theoretical description. Here we provide the first direct experimental measurement of the band structure along the synthetic dimension. By dynamically modulating a ring resonator at frequencies commensurate with its mode spacing, we realize a periodically driven system with a synthetic frequency dimension lattice. The strength and range of the couplings along the lattice can be dynamically reconfigured by changing the amplitude and frequency of modulation. We show theoretically and demonstrate experimentally that time-resolved transmission measurements of this system result in a direct "read out" of its band structure. We also show how long-range coupling, photonic gauge potentials and nonreciprocal bands can be realized in the system by simply incorporating additional frequency drives, enabling great flexibility in engineering the band structure.

physics.optics↗

arXiv2018

Experimental demonstration of dynamical input isolation in nonadiabatically modulated photonic cavities

Modulated optical cavities have been proposed and demonstrated for applications in communications, laser frequency stabilization, microwave-to-optical conversion and frequency comb generation. However, most studies are restricted to the adiabatic regime, where either the maximum excursion of the modulation or the modulation frequency itself is below the linewidth of the cavity. Here, using a fiber ring resonator with an embedded electro-optic phase modulator, we investigate the nonadiabatic regime. By strongly driving the modulator at frequencies that are significantly smaller than the free-spectral range of the ring resonator, but well beyond the linewidth of the resonator, we experimentally observe counterintuitive behavior predicted in a recent theoretical study by Minkov et al. [APL Photonics 2, 076101 (2017)], such as the complete suppression of drop-port transmission even when the input laser wavelength is on resonance with the optical cavity. This can be understood as dynamical isolation of the cavity from the input light. We also show qualitative differences in the steady-state responses of the system between the adiabatic and nonadiabatic limits. Our experiments probe a seldom explored regime of operation that is promising for applications in integrated photonic systems with current state-of-the-art technology.

physics.optics↗

arXiv2017

All-optical mode unscrambling on a silicon photonic chip

Propagation of light beams through scattering or multimode systems may lead to randomization of the spatial coherence of the light. Although information is not lost, its recovery requires a coherent interferometric reconstruction of the original signals, which have been scrambled into the modes of the scattering system. Here, we show that we can automatically unscramble four optical beams that have been arbitrarily mixed in a multimode waveguide, undoing the scattering and mixing between the spatial modes through a mesh of silicon photonics Mach-Zehnder interferometers. Using embedded transparent detectors and a progressive tuning algorithm, the mesh self-configures automatically and reset itself after significantly perturbing the mixing, without turning off the beams. We demonstrate the recovery of four separate 10 Gbits/s information channels, with residual cross-talk between beams of -20dB. This principle of self-configuring and self-resetting in optical systems should be applicable in a wide range of optical applications.

physics.ins-det↗