arXiv subjects

Daniel Pérez-López

Publications and source records attributed to Daniel Pérez-López.

6 recordsLinked to original sources

arXiv2026

Universal Neural Network Based Calibration and Control of Programmable Classical and Quantum Photonic Integrated Processors

Efficient calibration and control of programmable photonic integrated circuits are fundamental for scaling quantum and classical optical computing processors. While neural network-based models offer an architecture-agnostic solution, existing approaches suffer from limited learning and generalization capabilities due to the many-to-one mapping problem between sets of control signals and optical responses, and biased training datasets derived from uniform current sampling. In this work, we propose a universal calibration and control framework employing tandem neural networks combined with two novel data generation strategies: architecture-aware sampling based on Haar measure principles, and optimized sampling, a physics-agnostic approach utilizing differential evolution. We experimentally validate these methods on 3x3 and 4x4 coherent MZI meshes, demonstrating that our approach addresses the sampling bias inherent in previous works. When evaluated using random unitary matrices, our solution outperforms standard uniform sampling baselines by ~2 bits of precision. Furthermore, we experimentally extend the application of this framework to coherent detection, achieving precise control over both amplitude and phase, and validate its impact on photonic neural network tasks.

physics.optics↗

arXiv2026

High-Speed Non-Volatile Barium Titanate Field Programmable Photonic Gate Array

Programmable integrated photonics aims to replicate the versatility of field-programmable gate arrays in the optical domain. However, scaling these systems has been prevented by the high power consumption and thermal crosstalk of conventional volatile phase shifters. Here, we demonstrate the first non-volatile field-programmable photonic gate array, implemented on a hybrid silicon-barium titanate platform. Unlike traditional thermo-optic devices that require constant power to maintain a state, our device utilizes ferroelectric domain switching to provide non-volatile memory, allowing optical circuits to be programmed and retained without any holding power or electrical bias. The hexagonal waveguide mesh integrates 58 programmable unit cells and 116 actuators, achieving nanosecond-scale switching speeds of 80 nanoseconds while reducing static power consumption to negligible levels (560 nanowatts per π phase shift). To validate this platform, we configured the mesh to perform diverse signal processing functions, including tunable filtering, 4x4 linear unitary transformations, and optical routing. This work establishes non-volatile ferroelectric silicon photonics as a scalable, heat-free platform essential for the next generation of energy-efficient photonic computing.

physics.optics↗

arXiv2024

Programmable Photonic Extreme Learning Machines

Photonic neural networks offer a promising alternative to traditional electronic systems for machine learning accelerators due to their low latency and energy efficiency. However, the challenge of implementing the backpropagation algorithm during training has limited their development. To address this, alternative machine learning schemes, such as extreme learning machines (ELMs), have been proposed. ELMs use a random hidden layer to increase the feature space dimensionality, requiring only the output layer to be trained through linear regression, thus reducing training complexity. Here, we experimentally demonstrate a programmable photonic extreme learning machine (PPELM) using a hexagonal waveguide mesh, and which enables to program directly on chip the input feature vector and the random hidden layer. Our system also permits to apply the nonlinearity directly on-chip by using the systems integrated photodetecting elements. Using the PPELM we solved successfully three different complex classification tasks. Additioanlly, we also propose and demonstrate two techniques to increase the accuracy of the models and reduce their variability using an evolutionary algorithm and a wavelength division multiplexing approach, obtaining excellent performance. Our results show that programmable photonic processors may become a feasible way to train competitive machine learning models on a versatile and compact platform.

physics.optics↗

arXiv2024

Software-defined optical networking applications enabled by programmable integrated photonics

Data center networks are experiencing unprecedented exponential growth, mostly driven by the continuous computing demands in machine learning and artificial intelligence algorithms. Within this realm, optical networking offers numerous advantages, including low latency, energy efficiency, and bandwidth transparency, positioning it as a compelling alternative to its electronic counterparts. In this work, we showcase a range of software-defined optical networking applications deployed on a general-purpose programmable integrated photonic processor. Leveraging graph-based theory, we experimentally demonstrate dynamic optical interconnects, circuit switching, and multicasting on the same photonic platform, yielding remarkable results in terms of crosstalk and reconfiguration speed. Our approach harnesses the benefits of reconfigurability and reliability, paving the way for a new generation of high-performance optical devices tailored for data center and computing clusters.

cs.NI↗

arXiv2022

Linearization of a dual-parallel Mach-Zehnder modulator using optical carrier band processing

The linearization of a microwave photonic link based on a dual-parallel Mach-Zehnder modulator is theoretically described and experimentally demonstrated. Up to four different radio frequency tones are considered in the study, which allow us to provide a complete mathematical description of all third-order distortion terms that arise at the photodetector. Simulations show that a complete linearization is obtained by properly tuning the DC bias voltages and processing the optical carrier and. As a result, a suppression of 17 dBm is experimentally obtained for the third-order distortion terms, as well as a SDFR improvement of 3 dB. The proposed linearization method enables the simultaneous modulation of four different signals without the need of additional radio frequency components, which is desirable to its implementation in integrated optics and makes it suitable for several applications in microwave photonics.

physics.optics↗

arXiv2022

Analog Programmable-Photonic Computation

Digital electronics is a technological cornerstone in our modern society which has covered the increasing demand in computing power during the last decades thanks to a periodic doubling of transistor density and power efficiency in integrated circuits. Currently, such scaling laws are reaching their fundamental limits, leading to the emergence of a large gamut of applications that cannot be supported by digital electronics, specifically, those that involve real time analog multi-data processing, e.g., medical diagnostic imaging, robotic control and remote sensing, among others. In this scenario, an analog computing approach implemented in a real-time reconfigurable non-electronic hardware such as programmable integrated photonics (PIP) can be more efficient than digital electronics to perform these emerging applications. However, actual analog computing models such as quantum and neuromorphic computation were not conceived to extract the benefits of PIP technology (and integrated photonics in general). Here, we present the foundations of a new computation theory, termed Analog Programmable-Photonic Computation (APC), explicitly designed to unleash the full potential of PIP. Interestingly, APC enables overcoming basic theoretical and technological limitations of existing computational models, can be implemented in other technologies (e.g. in electronics, acoustics or using metamaterials) and, consequently, exhibits the potential to spark a ground-breaking impact on our information society.

cs.ET↗