arXiv subjects

Jose Roberto Rausell-Campo

Publications and source records attributed to Jose Roberto Rausell-Campo.

3 recordsLinked to original sources

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

Programming universal unitary transformations on a general-purpose silicon photonics platform

General-purpose programmable photonic processors provide a versatile platform for integrating diverse functionalities on a single chip. Leveraging a two-dimensional hexagonal waveguide mesh of Mach-Zehnder interferometers, these systems have demonstrated significant potential in microwave photonics applications. Additionally, they are a promising platform for creating unitary linear transformations, which are key elements in quantum computing and photonic neural networks. However, a general procedure for implementing these transformations on such systems has not been established yet. This work demonstrates the programming of universal unitary transformations on a general-purpose programmable photonic circuit with a hexagonal topology. We detail the steps to split the light on-chip, demonstrate that an equivalent structure to the Mach-Zehnder interferometer with one internal and one external phase shifter can be built in the hexagonal mesh, and program both the triangular and rectangular architectures for matrix multiplication. We recalibrate the system to account for passive phase deviations. Experimental programming of 3x3 and 4x4 random unitary matrices yields fidelities > 98% and bit precisions over 5 bits. To the best of our knowledge, this is the first time that random unitary matrices are demonstrated on a general-purpose photonic processor and pave the way for the implementation of programmable photonic circuits in optical computing and signal processing systems.

physics.optics↗