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Odile Liboiron-Ladouceur

Publications and source records attributed to Odile Liboiron-Ladouceur.

8 recordsLinked to original sources
arXiv2026

Gen-Fab: A Variation-Aware Generative Model for Predicting Fabrication Variations in Nanophotonic Devices

Silicon photonic devices often exhibit fabrication-induced variations such as over-etching, underetching, and corner rounding, which can significantly alter device performance. These variations are non-uniform and are influenced by feature size and shape. Accurate digital twins are therefore needed to predict the range of possible fabricated outcomes for a given design. In this paper, we introduce Gen-Fab, a conditional generative adversarial network (cGAN) based on Pix2Pix to predict and model uncertainty in photonic fabrication outcomes. The proposed method takes a design layout (in GDS format) as input and produces diverse high-resolution predictions similar to scanning electron microscope (SEM) images of fabricated devices, capturing the range of process variations at the nanometer scale. To enable one-to-many mapping, we inject a latent noise vector at the model bottleneck. We compare Gen-Fab against three baselines: (1) a deterministic U-Net predictor, (2) an inference-time Monte Carlo Dropout U-Net, and (3) an ensemble of varied U-Nets. Evaluations on an out-of-distribution dataset of fabricated photonic test structures demonstrate that Gen-Fab outperforms all baselines in both accuracy and uncertainty modeling. An additional distribution shift analysis further confirms its strong generalization to unseen fabrication geometries. Gen-Fab achieves the highest intersection-over-union (IoU) score of 89.8%, outperforming the deterministic U-Net (85.3%), the MC-Dropout U-Net (83.4%), and varying U-Nets (85.8%). It also better aligns with the distribution of real fabrication outcomes, achieving lower Kullback-Leibler divergence and Wasserstein distance.

cs.CV↗
arXiv2024

SEMU-Net: A Segmentation-based Corrector for Fabrication Process Variations of Nanophotonics with Microscopic Images

Integrated silicon photonic devices, which manipulate light to transmit and process information on a silicon-on-insulator chip, are highly sensitive to structural variations. Minor deviations during nanofabrication-the precise process of building structures at the nanometer scale-such as over- or under-etching, corner rounding, and unintended defects, can significantly impact performance. To address these challenges, we introduce SEMU-Net, a comprehensive set of methods that automatically segments scanning electron microscope images (SEM) and uses them to train two deep neural network models based on U-Net and its variants. The predictor model anticipates fabrication-induced variations, while the corrector model adjusts the design to address these issues, ensuring that the final fabricated structures closely align with the intended specifications. Experimental results show that the segmentation U-Net reaches an average IoU score of 99.30%, while the corrector attention U-Net in a tandem architecture achieves an average IoU score of 98.67%.

cs.CV↗
arXiv2024

Recent Advancements in Mode Division Multiplexing for Communication and Computation in Silicon Photonics

Mode Division Multiplexing (MDM) is a technique used over the past decade in Silicon Photonics (SiPh) to incorporate more data into communication links by employing higher-order transverse electric or transverse magnetic modes. MDM was primarily used in optical communication; however, in recent years, there have been several applications of MDM in optical computing, including both classical and quantum computing. Although MDM has shown great promise for increasing the throughput of optical communication and the accuracy and fidelity of optical computation, there are a few challenges towards expanding its applications. One major challenge is the lack of process design kits (PDKs) and building block libraries compatible with standard SiPh foundries. Here, we present a comprehensive library of MDM components developed using classical and inverse design, compatible with standard 220 nm SiPh foundries. The library includes thermo-optic phase shifters, mode multiplexers and demultiplexers, mode converters, mode exchangers, and multi-mode interference couplers. We also discuss our recent achievements in MDM for datacom, classical and quantum optical computing, including a mode-selective switch for mode-reconfigurable optical add-drop multiplexer (ROADM), multimode multiply-accumulate operation, and multimode photonic quantum processors.

physics.optics↗
arXiv2023

Silicon Photonics Mode-Selective Phase Shifter

A novel mode-selective thermo-optic phase shifter (MS-TOPS) enabled by subwavelength grating (SWG) structures is proposed and experimentally demonstrated on a 220 nm waveguide thick silicon photonics chip for the first two quasi-transverse electric modes (TE0, TE1). Mode-selective relative phase manipulation of modes unlocks several processing tasks in mode division multiplexing systems. This integrated solution provides a direct phase manipulation of modes without converting them to their fundamental modes. A Mach-Zehnder interferometer is deployed as a test structure incorporating the proposed MS-TOPS in one arm and a mode-insensitive thermo-optic phase shifter (MI-TOPS) in another. The effect of the SWG duty cycle ratio is investigated by both numerical simulations and experimental measurements. A mode-selectivity of 1.44 is experimentally demonstrated. In other words, the thermo-optic coefficient of TE0 is 44% larger than the one for TE1. The phase shifter's insertion loss is at most 2.5 dB and a worst-case crosstalk of -13.1 dB over a 40 nm wavelength range from 1520 to 1560 nm. A cascaded configuration of the proposed MS-TOPS and an MI-TOPS provides sufficient degrees of freedom to manipulate the relative phase of each mode independently. Potential numerous applications of such devices include optical switching, multimode quantum optical processors, and scaling-up conventional optical processors with a mode selective building block.

physics.optics↗
arXiv2023

Improving Fabrication Fidelity of Integrated Nanophotonic Devices Using Deep Learning

Next-generation integrated nanophotonic device designs leverage advanced optimization techniques such as inverse design and topology optimization which achieve high performance and extreme miniaturization by optimizing a massively complex design space enabled by small feature sizes. However, unless the optimization is heavily constrained, the generated small features are not reliably fabricated, leading to optical performance degradation. Even for simpler, conventional designs, fabrication-induced performance degradation still occurs. The degree of deviation from the original design not only depends on the size and shape of its features, but also on the distribution of features and the surrounding environment, presenting complex, proximity-dependent behavior. Without proprietary fabrication process specifications, design corrections can only be made after calibrating fabrication runs take place. In this work, we introduce a general deep machine learning model that automatically corrects photonic device design layouts prior to first fabrication. Only a small set of scanning electron microscopy images of engineered training features are required to create the deep learning model. With correction, the outcome of the fabricated layout is closer to what is intended, and thus so too is the performance of the design. Without modifying the nanofabrication process, adding significant computation in design, or requiring proprietary process specifications, we believe our model opens the door to new levels of reliability and performance in next-generation photonic circuits.

cs.LG↗
arXiv2023

Addressing the programming challenges of practical interferometric mesh based optical processors

We demonstrate a novel mesh of Mach-Zehnder interferometers (MZIs) for programmable optical processors. The proposed mesh, referred to as Bokun mesh, is an architecture that merges the attributes of the prior topologies Diamond and Clements. Similar to Diamond, Bokun provides diagonal paths passing through every individual MZI enabling direct phase monitoring. However, unlike Diamond and similar to Clements, Bokun maintains a minimum optical depth leading to better scalability. Providing the monitoring option, Bokun's programming is faster improving the total energy efficiency of the processor. The performance of Bokun mesh enabled by an optimal optical depth is also more resilient to the loss and fabrication imperfections compared to architectures with longer depth such as Reck and Diamond. Employing an efficient programming scheme, the proposed architecture improves energy efficiency by 83% maintaining the same computation accuracy for weight matrix changes at 2 kHz.

cs.ET↗
arXiv2022

On-chip Optical Phase Monitoring in Multi-Transverse-Mode Integrated Silicon-based Optical Processors

We design a Multi-Transverse-Mode Optical Processor (MTMOP) on 220 nm thick Silicon Photonics exploiting the first two quasi-transverse electric modes (TE0 and TE1). The objective is to measure the optical phase, required for programming the optical processor, without use of conventional optical phase detection techniques (e.g., coherent detection). In the proposed design, we use a novel but simple building block that converts the optical phase to optical power. Mode TE0 carries the main optical signal while mode TE1 is for programming purposes. The MTMOP operation relies on the fact that the group velocity of TE0 and TE1 propagating through a mode-sensitive phase shifter are different. An unbalanced Mach-Zehnder interferometer (MZI) consists of a mode-sensitive and mode-insensitive phase shifters in the two arms. We set the bias of the phase shifters so that TE0 propagating in the two arms constructively interfere while this will not be the case for TE1. Hence, we detect the phase shift applied to TE0 by measuring the variation in the optical power of TE1. To the best of our knowledge, this design is the first attempt towards realizing a programmable optical processor with fully integrated programming unit exploiting multimode silicon photonics.

physics.optics↗