arXiv subjects

Anthony Rizzo

Publications and source records attributed to Anthony Rizzo.

8 recordsLinked to original sources

arXiv2026

Physics-Based Flow Matching for Full-Field Prediction of Silicon Photonic Devices

Designing photonic integrated circuits requires accurate electromagnetic field simulations, which remain computationally expensive even for simple device geometries. We present PIC-Flow, a generative neural surrogate that predicts electromagnetic field distributions for photonic devices given their geometry and operating wavelength as an alternative to costly finite-difference time-domain (FDTD) simulations. Our approach combines three key ideas: (i) conditional flow matching as the generative framework, learning a velocity field that transports Gaussian noise to physically valid field solutions; (ii) a real-valued U-Net operating on split real and imaginary field channels; and (iii) physics-constrained training through a Helmholtz residual loss enforcing $\nabla^2 E_z + k_0^2 \varepsilon E_z = 0$. We introduce an interface-aware masking scheme for the Helmholtz residual that excludes dielectric boundary pixels where finite-difference stencil errors dominate, yielding a physically meaningful compliance metric. The data set consists of 22,500 ground-truth FDTD simulations split evenly between multimode interferometers, Y-branches, and directional couplers at $\lambda=1.55\,\mu$m in an 80/10/10 split between training, validation, and test sets. We evaluate ablations on the network against the held out test devices and also show that the model generalizes to held out device classes such as S-bends, tapers, and cascaded Y-branches. Rather than a drop-in replacement for FDTD, this work establishes a foundation that, with broader data coverage, more compute, and further training optimization, could scale toward broadband, device-agnostic field prediction with dramatically improved runtime for rapid design-space exploration of complex photonic devices and circuits.

physics.optics↗

arXiv2025

Highly Uniform Thermally Undercut Silicon Photonic Devices in a 300 mm CMOS Foundry Process

Silicon photonic devices fundamental to high-density wavelength-division multiplexed (DWDM) optical links and photonic switching networks, such as resonant modulators and Mach-Zehnder interferometers (MZIs), are highly sensitive to fabrication variations and operational temperature swings. However, thermal tuning to compensate for fabrication and operational temperature variations can result in prohibitive power consumption, challenging the scalability of energy-efficient photonic integrated circuits (PICs). In this work, we develop and demonstrate a wafer-scale thermal undercut process in a 300 mm complementary metal oxide semiconductor (CMOS) foundry that dramatically improves the thermal isolation of thermo-optic devices by selectively removing substrate material beneath the waveguides and resonators. This approach significantly reduces the power required for thermal tuning across multiple device architectures, achieving almost a 5$\times$ improvement in tuning efficiency in a state-of-the-art 4.5 $\mu$m radius microdisk modulator and a 40$\times$ improvement in efficiency for a MZI phase shifter. To the best of the authors' knowledge, we demonstrate the first wafer-scale comparison of non-undercut and undercut silicon photonic devices using comprehensive wafer-scale measurements across 64 reticles of a 300 mm silicon-on-insulator (SOI) wafer. Further, we demonstrate a comprehensive wafer-scale analysis of the influence of undercut trench opening geometry on device tuning efficiency. Notably, we observe highly uniform performance across the full 300 mm wafer for multiple device types, emphasizing that our process can be scaled to large-scale photonic circuits with high yield. These results open new opportunities for large-scale integrated photonic circuits using thermo-optic devices, paving the way for scalable, low-power silicon photonic systems.

physics.optics↗

arXiv2024

Low-Crosstalk, Silicon-Fabricated Optical Waveguides for Laser Delivery to Matter Qubits

Reliable control of quantum information in matter-based qubits requires precisely applied external fields, and unaccounted for spatial cross-talk of these fields between adjacent qubits leads to loss of fidelity. We report a CMOS foundry-produced, micro-fabricated silicon nitride (Si3N4) optical waveguide for addressing a chain of eight, unequally-spaced trapped barium ions with crosstalk compatible with scalable quantum information processing. The crosstalk mitigation techniques incorporated into the chip design result in a reduction of the measured optical field by at least 50.8(1.3) dB between adjacent waveguide outputs near 650 nm and similar behavior for devices designed for 493 nm and 585 nm. The waveguide outputs near 650 nm, along with a global laser near 493 nm were used to laser-cool a chain of eight barium-138 ions, and a camera imaged the resulting fluorescence at 493 nm.

quant-ph↗

arXiv2023

3D photonics for ultra-low energy, high bandwidth-density chip data links

Artificial intelligence (AI) hardware is positioned to unlock revolutionary computational abilities across diverse fields ranging from fundamental science [1] to medicine [2] and environmental science [3] by leveraging advanced semiconductor chips interconnected in vast distributed networks. However, AI chip development has far outpaced that of the networks that connect them, as chip computation speeds have accelerated a thousandfold faster than communication bandwidth over the last two decades [4, 5]. This gap is the largest barrier for scaling AI performance [6, 7] and results from the disproportionately high energy expended to transmit data [8], which is two orders of magnitude more intensive than computing [9]. Here, we show a leveling of this long-standing discrepancy and achieve the lowest energy optical data link to date through dense 3D integration of photonic and electronic chips. At 120 fJ of consumed energy per communicated bit and 5.3 Tb/s bandwidth per square millimeter of chip area, our platform simultaneously achieves a twofold improvement in both energy consumption and bandwidth density relative to prior demonstrations [10, 11]. These improvements are realized through employing massively parallel 80 channel microresonator-based transmitter and receiver arrays operating at 10 Gb/s per channel, occupying a combined chip footprint of only 0.32 mm2. Furthermore, commercial complementary metal-oxide-semiconductor (CMOS) foundries fabricate both the electronic and photonic chips on 300 mm wafers, providing a clear avenue to volume scaling. Through these demonstrated ultra-energy efficient, high bandwidth data communication links, this work eliminates the bandwidth bottleneck between spatially distanced compute nodes and will enable a fundamentally new scale of future AI computing hardware without constraints on data locality.

physics.optics↗

arXiv2023

Process Variation-Aware Compact Model of Strip Waveguides for Photonic Circuit Simulation

We report a novel process variation-aware compact model of strip waveguides that is suitable for circuit-level simulation of waveguide-based process design kit (PDK) elements. The model is shown to describe both loss and -- using a novel expression for the thermo-optic effect in high index contrast materials -- the thermo-optic behavior of strip waveguides. A novel group extraction method enables modeling the effective index's ($n_{\mathrm{eff}}$) sensitivity to local process variations without the presumption of variation source. Use of Euler-bend Mach-Zehnder interferometers (MZIs) fabricated in a 300~mm wafer run allow model parameter extraction at widths up to 2.5~$\mu$m (highly multi-mode) with strong suppression of higher-order mode excitation. Experimental results prove the reported model can self-consistently describe waveguide phase, loss, and thermo-optic behavior across all measured devices over an unprecedented range of optical bandwidth, waveguide widths, and temperatures.

physics.optics↗

arXiv2022

Fabrication-Robust Silicon Photonic Devices in Standard Sub-Micron Silicon-on-Insulator Processes

Perturbations to the effective refractive index from nanometer-scale fabrication variations in waveguide geometry plague high index-contrast photonic platforms including the ubiquitous sub-micron silicon-on-insulator (SOI) process. Such variations are particularly troublesome for phase-sensitive devices such as interferometers and resonators, which exhibit drastic changes in performance as a result of these fabrication-induced phase errors. In this Letter, we propose and experimentally demonstrate a design methodology for dramatically reducing device sensitivity to silicon width variations. We apply this methodology to a highly phase-sensitive device, the ring-assisted Mach Zehnder interferometer (RAMZI), and show comparable performance and footprint to state-of-the-art devices while substantially reducing stochastic phase errors from etch variations. This decrease in sensitivity is directly realized as energy savings by significantly lowering the required corrective thermal tuning power, providing a promising path towards ultra-energy-efficient large-scale silicon photonic circuits.

physics.optics↗

arXiv2022

Massively Scalable Wavelength Diverse Integrated Photonic Linear Neuron

As computing resource demands continue to escalate in the face of big data, cloud-connectivity and the internet of things, it has become imperative to develop new low-power, scalable architectures. Neuromorphic photonics, or photonic neural networks, have become a feasible solution for the physical implementation of efficient algorithms directly on-chip. This application is primarily due to the linear nature of light and the scalability of silicon photonics, specifically leveraging the wide-scale complementary metal-oxide-semiconductor (CMOS) manufacturing infrastructure used to fabricate microelectronics chips. Current neuromorphic photonic implementations stem from two paradigms: wavelength coherent and incoherent. Here, we introduce a novel architecture that supports coherent and incoherent operation to increase the capability and capacity of photonic neural networks with a dramatic reduction in footprint compared to previous demonstrations. As a proof-of-principle, we experimentally demonstrate simple addition and subtraction operations on a foundry-fabricated silicon photonic chip. Additionally, we experimentally validate an on-chip network to predict the logical 2-bit gates AND, OR, and XOR to accuracies of $96.8\%, 99\%,$ and $98.5\%$, respectively. This architecture is compatible with highly wavelength parallel sources, enabling massively scalable photonic neural networks.

cs.ET↗

arXiv2021

Integrated Kerr frequency comb-driven silicon photonic transmitter

The exponential growth of computing needs for artificial intelligence and machine learning has had a dramatic impact on data centre energy consumption, which has risen to environmentally significant levels. Using light to send information between compute nodes can dramatically decrease this energy consumption while simultaneously increasing bandwidth. Through wavelength-division multiplexing with chip-based microresonator Kerr frequency combs, independent information channels can be encoded onto many distinct colours of light in the same optical fibre for massively parallel data transmission with low energy. While previous demonstrations have relied on benchtop equipment for filtering and modulating Kerr comb wavelength channels, data centre interconnects require a compact on-chip form factor for these operations. Here, we demonstrate the first integrated silicon photonic transmitter using a Kerr comb source. The demonstrated architecture is scalable to hundreds of wavelength channels, enabling a fundamentally new class of massively parallel terabit-scale optical interconnects for future green hyperscale data centres.

physics.app-ph↗