Measuring, processing, and generating partially coherent light with self-configuring optics

Light Science & Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: Sept. 20, 2024

Language: Английский

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Programmable integrated photonic coherent matrix: Principle, configuring, and applications

Applied Physics Reviews, Journal Year: 2024, Volume and Issue: 11(1)

Published: Jan. 31, 2024

Every multi-input multi-output linear optical system can be deemed as a matrix multiplier that carries out desired transformation on the input information, such imaging, modulation, and computing. The strong programmability of has been explored proved to able bring more flexibility greater possibilities applications signal processing general digital analog Furthermore, burgeoning integrated photonics with advanced manufacturing light manipulating technology pave way for large-scale reconfigurable photonic coherent matrix. This paper reviews programmable in platform. First, theoretical basis optimizing methods three types (Mach–Zehnder interferometer mesh, multi-plane diffraction, crossbar array) are introduced. Next, we overview configuring method this their processing, neural network, logic operation, recurrent acceleration, quantum computing comprehensively reviewed. Finally, challenges opportunities discussed.

Language: Английский

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Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks

Physical Review Letters, Journal Year: 2025, Volume and Issue: 134(1)

Published: Jan. 7, 2025

The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical neural networks (PNNs) offer efficient neuromorphic information processing by harnessing innate power physical processes; however, training their weight parameters is computationally expensive. We propose a approach substantially reducing this cost. Our merges an optimal control method continuous-time dynamical systems with biologically plausible method-direct feedback alignment. In addition to reduction time, achieves robust even under measurement errors and noise without requiring detailed system information. effectiveness was numerically experimentally verified in optoelectronic delay system. significantly extends range practically usable as PNNs.

Language: Английский

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Asymmetrical estimator for training encapsulated deep photonic neural networks

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 3, 2025

Abstract Photonic neural networks (PNNs) are fast in-propagation and high bandwidth paradigms that aim to popularize reproducible NN acceleration with higher efficiency lower cost. However, the training of PNN is known be challenging, where device-to-device system-to-system variations create imperfect knowledge PNN. Despite backpropagation (BP)-based algorithms being industry standard for their robustness, generality, gradient convergence digital training, existing PNN-BP methods rely heavily on accurate intermediate state extraction or extensive computational resources deep PNNs (DPNNs). The truncated photonic signal propagation computation overhead bottleneck DPNN’s operation increase system construction Here, we introduce asymmetrical (AsyT) method, tailored encapsulated DPNNs, preserved in analogue domain entire structure. AsyT offers a lightweight solution DPNNs minimum readouts, energy-efficient operation, footprint. AsyT’s ease error tolerance, generality promote widened operational scenario despite fabrication controls. We demonstrated DPNN integrated chips, repeatably enhancing performance from in-silico BP different network structures datasets.

Language: Английский

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Physical neural networks with self-learning capabilities

Science China Physics Mechanics and Astronomy, Journal Year: 2024, Volume and Issue: 67(8)

Published: July 22, 2024

Physical neural networks are artificial that mimic synapses and neurons using physical systems or materials. These harness the distinctive characteristics of to carry out computations effectively, potentially surpassing constraints conventional digital networks. A recent advancement known as ``physical self-learning'' aims achieve learning through intrinsic processes rather than relying on external computations. This article offers a comprehensive review progress made in implementing self-learning across various systems. Prevailing strategies discussed contribute realization self-learning. Despite challenges understanding fundamental mechanism learning, this work highlights towards constructing intelligent hardware from ground up, incorporating embedded self-organizing self-adaptive dynamics

Language: Английский

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A deep neural network model for parameter identification in deep drawing metal forming process

Journal of Manufacturing Processes, Journal Year: 2024, Volume and Issue: 133, P. 380 - 394

Published: Nov. 29, 2024

Language: Английский

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Annealing-inspired training of an optical neural network with ternary weights

Communications Physics, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 13, 2025

Language: Английский

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Universal approximation theorem for nonlinear resistive networks

Physical Review Applied, Journal Year: 2025, Volume and Issue: 23(4)

Published: April 3, 2025

Language: Английский

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Active control of electroacoustic resonators in the audible regime: control strategies and airborne applications

Published: April 7, 2025

Abstract Passive sound mitigation techniques have garnered attention whether for absorption, isolation, reverberation or new wave phenomena observation. In parallel, a wide range of research has been devoted to active control strategies, which complement passive techniques, particularly low-frequency. We review the main related with airborne acoustic in audible regime, emphasizing electrodynamic loudspeakers and piezo-diaphragms, their applications. conclude by discussing perspectives this evolving field.

Language: Английский

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Perspective on nonvolatile magnon-signal storage and in-memory computation for low-power consuming magnonics

Applied Physics Letters, Journal Year: 2025, Volume and Issue: 126(16)

Published: April 21, 2025

Magnons are the quanta of spin waves and transport angular momenta through magnetically ordered materials. They can be used to distribute control on-chip GHz signals without charge flow, thereby avoiding Joule heating. Beyond multiplexed signal processing, filtering, Boolean logic, they allow for hardware implementation neural networks exploiting cascaded magnon scattering on nanoscale. A game-changing boost is expected if nonvolatile magnon-signal storage in-memory computation schemes become realistic. We outline recent progress in experimental research micromagnetic modeling toward these goals before sketching remaining challenges.

Language: Английский

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