Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2608.10582

Direct experimental measurement of femtonewton-scale momentum transfer force from electron beams

Chunbo Lin·Xinggang Shang·Xijun Li·Xiaoyu Sun·Kang Zhao·Xujie Wang·Yang Yu·Wenjing Cao·Min Qiu
View original source ↗Read source document ↗Export metadata ↓

Abstract

Electron beams (e-beams) are ubiquitous in imaging, patterning, and propulsion. This prevalence is rooted in the profound mastery of their wave-particle duality and energy-transfer pathways. Yet, a fundamental dimension remains largely unexplored: while the mechanical effect (i.e., the momentum transfer to a target) is theoretically known, quantification of its femtonewton-range force has remained elusive. This discrepancy represents a missing piece of the puzzle toward a comprehensive understanding of e-beam-matter interactions, and ultimately limits the multi-dimensional exploitation of e-beams. A force sensor combining femtonewton sensitivity, immunity to electromagnetic noise, compatibility with vacuum, and absolute calibration is critical to bridge the gap between theory and experiment. Here the FINEST (Femtonewton Interferometric Nanomechanical Electron-beam Sensing Technology) sensor is proposed and successfully tested to measure the force of an e-beam. FINEST is an optical-pressure-calibrated 3D spring-type optical sensor that operates reliably under e-beam conditions. Femtonewton-scale forces from 2-30 keV e-beams are directly measured, ranging from 505 fN to 13 pN. Both linear scaling with beam current and a non-monotonic energy dependence (peaking near 10 keV) are observed. Based on this calibrated force, the mechanical contribution to e-beam ice etching was quantitatively confirmed; its effect is orders of magnitude lower than the total etch depth and lacks noticeable energy dependence. By achieving the first direct experimental measurement of e-beam momentum transfer, this work adds a long-missing dimension to the physical landscape of e-beam processes. These findings provide a quantitative basis for furthering the multi-dimensional exploitation of e-beams, potentially transforming our approach to precision nanofabrication, sensing, and fundamental electron physics research.

Explore related subjects

physics.optics

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chunbo Lin, Xinggang Shang, Xijun Li, Xiaoyu Sun, Kang Zhao, Xujie Wang, Yang Yu, Wenjing Cao, Min Qiu. 2026-08-11. Direct experimental measurement of femtonewton-scale momentum transfer force from electron beams. https://arxiv.org/abs/2608.10582

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Active Fourier Spectroscopy: From fixed sampling protocols to adaptive measurement

Fourier-transform spectroscopy (FTS) is based on the Nyquist-Shannon theorem, which dictates a strict sampling protocol for the measurement of continuous signals. Here we introduce Active Fourier Spectroscopy (AFS), a generalization of FTS in which measurements are still taken in the Fourier-conjugate delay domain, but where the most informative acquisition points are selected adaptively and the spectrum is reconstructed by sequential, uncertainty-aware inference rather than by fixed-grid Fourier inversion. Our approach relies on a theoretical insight that the Nyquist condition applies only under complete ignorance about the system under investigation, and shows how to systematically improve upon it when prior knowledge exists. Because AFS recovers conventional FTS whenever the prior is uninformative, its performance never falls below that of conventional sampling. We demonstrate AFS for optical spectroscopy in three settings vital to research and medicine: clinical FTIR-based blood plasma analysis, optical metrology of structured laser beams, and hyperspectral imaging with an RGB camera. In each case AFS reaches a higher target reconstruction quality from far fewer acquisitions and provides complete uncertainty quantification in real time, propagated rigorously through downstream analysis.

physics.optics↗
arXiv2026

Memory in Integrated Photonic Neural Networks: From Physical Mechanisms to Neuromorphic Architectures

The rapid scaling of artificial neural networks has exposed fundamental limitations of conventional von Neumann computing architectures. In these systems, the physical separation between memory and processing creates a bottleneck, as computational capabilities outpace the ability of memory and interconnects to supply and retrieve data. In contrast, biological neural systems inherently co-localize computation and memory through distributed, dynamical processes. Neuromorphic computing seeks to emulate this paradigm by leveraging physical substrates whose intrinsic dynamics simultaneously encode and process information. Among emerging platforms, silicon photoncis offer a compelling approach due to its high bandwidth, low-loss propagation, and inherent parallelism. This review examines the role of memory in integrated photonic neuromorphic systems, with emphasis on the physical mechanisms that provide volatile (short-term) and non-volatile (long-term) memory in silicon-on-insulator and hybrid silicon-on-insulator platforms. Drawing inspiration from digital, biological, and photonic memory architectures, we classify existing approaches based on their underlying physical principles. We cover implementations ranging from delay lines and slow-light structures to multistable dynamics and structural memory based on charge trapping and phase-change materials. We then discuss how these mechanisms support photonic neural network architectures, including feed-forward, reservoir computing, spiking and hybrid optoelectronic recurrent systems, and assess their relevance for time-dependent singal-processing tasks such as channel equalization in telecommunications. This review aims to establish a unified framework for understanding memory and learning in neuromorphic photonics and outlines key challenges and opportunities for scalable, energy-efficient neuromorphic hardware.

physics.optics↗
arXiv2026

Exoplanet Detection Using Adaptive Quantum-Optimal Measurement

Detecting terrestrial exoplanets in the habitable zones of nearby stars remains a critical challenge. Such planets can be \(10^8\) to \(10^{10}\) times fainter than their host stars and lie at diffraction-limited angular separations, where starlight strongly obscures the companion signal. Here we present an adaptive quantum measurement method for estimating the number, positions, and brightnesses of mutually incoherent point sources in the sub-Rayleigh, ultra-high-contrast regime, operating at contrasts down to \(10^{-8}\) -- five orders of magnitude beyond previous quantum imaging approaches to exoplanet detection. The method adopts a spatial-mode basis that is updated to maximize the quantum Fisher information per detected photon. Estimation is performed by maximum likelihood in log-brightness coordinates, and the source count is determined by Bayesian-information-criterion (BIC) model selection directly from photon-count statistics, without a tunable detection threshold. For point sources within sub-Rayleigh separations and with brightness ratios spanning eight orders of magnitude, the method reconstructs complete scenes with a mean success rate of \(72.5\%\). Furthermore, it is robust to misalignment, maintaining a \(71.3\%\) success rate under offsets of up to six pixels. These results demonstrate that terrestrial exoplanets can be detected below the Rayleigh limit, a regime previously inaccessible to direct imaging.

physics.optics↗