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arXiv 2602.14124cond-mat.mes-hall
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强耦合纳米机电系统中的非线性效应

Nonlinear effects in a strongly coupled Nanoelectromechanical System

Politecnico di Milano(米兰理工大学);Isfahan University of Technology(伊斯法罕理工大学)

Narges Tarakameh Samani, Farhad Shahbazi, Mehdi Abdi

AI总结:

本研究通过电压依赖的哈密顿框架,探索强耦合纳机电系统中非线性效应,揭示了频率梳形成与稳定性过渡之间的关系,并建立了可预测的动态工程框架。

摘要:

在微机电和纳机电系统中控制非线性效应对于解锁其在传感、信号处理和频率控制中的全部潜力至关重要。在本研究中,我们为具有两个强耦合振动模式的纳机电谐振器开发了一个电压依赖的哈密顿框架,这代表了纳米弦平台。系统模式频率和耦合通过直流电压电静电方式调节,这也控制了相互作用的强度。我们的理论模型在没有交流驱动的情况下重现了实验观测到的避险交叉,并在应用参数驱动时生成可调谐的频率梳光谱。通过扫描直流电压,我们生成了一个相图,将梳形形成和尖锐区域边界与底层的分岔、多稳态和吸引子切换联系起来。基于库拉托夫斯基有序参数的相分辨诊断,结合自相关和庞加莱分析,量化了这些过渡附近的一致性和临界减缓。我们进一步探讨了非线性耦合、参数激励和稳定性过渡之间的关系,以相关实验设备为背景,并建立了工程纳机电谐振器的动态框架,这些谐振器提供了增强的可调性、功能性和与实验结果的预测联系。

英文摘要:

Controlling nonlinear effects in micro- and nano-electro-mechanical systems is essential for unlocking their full potential in sensing, signal processing, and frequency control. In this study, we develop a voltage-dependent Hamiltonian framework for a nanoelectromechanical resonator with two strongly coupled vibrational modes, representative of a nanostring platform. The mode frequencies and couplings of the system are tuned electrostatically using a DC voltage, which also controls the strength of the interactions. Our theoretical model reproduces the experimentally observed avoided crossing in the absence of an AC drive and generates tunable frequency-comb spectra when a parametric drive is applied. By scanning the DC voltage, we generate a phase diagram that links comb formation and sharp regime boundaries to underlying bifurcations, multi-stability, and attractor switching. Phase-resolved diagnostics based on a Kuramoto order parameter, together with autocorrelation and Poincaré analyses, quantify coherence and critical slowing down near these transitions. We further explore the relationship between nonlinear coupling, parametric excitation, and stability transitions within a single device of experimental relevance and establish a dynamical framework for engineering nanoelectromechanical resonators that offer enhanced tunability, functionality, and a predictive link to experimental outcomes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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