arXiv subjects

Azka Maula Iskandar Muda

Publications and source records attributed to Azka Maula Iskandar Muda.

5 recordsLinked to original sources

arXiv2026

Deep Inverse-Designed Nanophotonic Processors with Structural Nonlinearity from Repeated Phase Encoding

Passive nanophotonic scattering regions implement linear optical transformations, and cascading input-independent regions alone does not create functional depth because the resulting transformations collapse into a single linear operator. Here, we introduce repeated phase encoding between inverse-designed passive transformations to generate an input-conditioned multilayer optical map without interlayer photodetection. Each re-encoding introduces additional phase-dependent optical pathways, producing structural nonlinearity with respect to the encoded variables while every scattering region remains passive and linear in the optical field. Under a controlled MNIST depth sweep, classification accuracy increases from 83.53\% with one layer to 93.61\% with seven layers, whereas the input-independent passive control saturates at 86.34\%. The depth trend also persists in a time-multiplexed CIFAR-10 patch model. We further realize three jointly trained $16\times16$ transformations, each independently implemented as an inverse-designed nanophotonic region, with relative complex transmission errors of 7.63\%, 7.59\%, and 8.80\%. The reconstructed electromagnetic stack reaches 91.79\% accuracy after phase calibration, compared with 91.95\% for its surrogate model. These results establish repeated input encoding as a route to functional depth in compact inverse-designed nanophotonic processors.

physics.optics↗

arXiv2026

Scalable Photonic Neural Networks via Surrogate Scattering-Matrix Inverse Design

Inverse-designed nanophotonic media are a promising platform for compact optical neural networks, but training them end to end is expensive because each adjoint iteration couples the full-wave solver to the dataset minibatch, so the number of electromagnetic simulations scales with both the network depth and the batch size. We introduce a two-stage surrogate workflow that decouples task learning from electromagnetic realization. In the first stage, the trainable optical block is represented as a passive complex matrix with bounded singular values and the classification task is solved directly in matrix space at negligible cost. In the second stage, the selected target operator is transferred to a fabrication-aware freeform device through an adjoint problem driven by a Frobenius-norm transmission residual and a reflection penalty, which removes the minibatch dependence from the full-wave loop and yields a smoother loss landscape than intensity-domain cross-entropy. We further introduce a banded-router architecture composed with a fixed evanescent-coupling region, which exploits the bandwidth-additive property of matrix products to realize dense effective operators within a design region roughly half as long as a fully local router would require. The framework is validated on three tasks. On MedMNIST, the realized all-optical classifier reproduces the surrogate accuracy within $0.6$ percentage points after only 20 adjoint epochs. On RSSCN7, the banded router plus evanescent stage improves test accuracy by more than 15 percentage points over a linear readout baseline. A Yin-Yang task confirms that the same framework supports nonlinear decision boundaries. These results indicate that surrogate-guided inverse design is a practical route to training compact photonic processors with simulation budgets orders of magnitude smaller than direct geometry-to-task pipelines.

physics.optics↗

arXiv2025

Engineering Rogue Waves via Multimode Interactions in Integrated Waveguides

We explore rogue wave formation in multimode silicon nitride (Si$_3$N$_4$) waveguides with multimode nonlinear Schrödinger equation-based simulations. Pure fundamental-mode excitation produces smooth propagation without extreme events, whereas higher-order modes or multimode superpositions yield heavy-tailed statistics with bursts exceeding the $8σ$ threshold. These results reveal that rogue wave generation in integrated waveguides is controlled not only by material properties such as nonlinearity and dispersion but also by modal excitation and intermodal nonlinear interactions. Our results identify modal control as a new degree of freedom for engineering extreme spatiotemporal events on photonic chips, with implications for on-chip supercontinuum generation, frequency combs, and nonlinear wave management.

physics.optics↗

arXiv2025

Spatiotemporal Nonlinear Pulse Dynamics in Multimode Silicon Nitride Waveguides

We present an open-source multimode nonlinear Schrödinger equation-based simulation to investigate spatiotemporal nonlinear pulse propagation in thin-film silicon nitride (SiN) waveguides. Using this framework, we analyze femtosecond pulse evolution under diverse excitation conditions in a 6 μm wide SiN waveguide supporting six TE modes. Our results reveal that mode selection and power distribution critically govern nonlinear coupling, soliton fission, and dispersive wave generation, leading to broadband spectra exceeding 3 μm. Our findings reveal that input mode engineering is a powerful strategy for tailoring ultrafast nonlinear dynamics in integrated photonic platforms, with applications in supercontinuum generation, frequency combs, and programmable nonlinear optics.

physics.optics↗

arXiv2024

Optical computing with supercontinuum generation in photonic crystal fibers

We introduce a novel photonic neural network using photonic crystal fibers, leveraging femtosecond pulse supercontinuum generation for optical computing. Investigating its efficacy across machine learning tasks, we uncover the crucial impact of nonlinear pulse propagation dynamics on network performance. Our findings show that octave-spanning supercontinuum generation results in loss of dataset variety due to many-to-one mapping, and optimal performance requires balancing optical nonlinearity. This study offers guidance for designing energy-efficient and high-performance photonic neural network architectures by explaining the interplay between nonlinear dynamics and optical computing.

physics.optics↗