STIR: Multistability and Chaos in a Driven Nanowire System

Abstract

During the nine-month period of this STIR project, three things were done: (1) we discovered anti-phase synchronization in microelectromechanical (MEM) systems, (2) we uncovered a number of complex dynamical phenomena in nanoelectromechanical (NEM) systems, and (3) we developed an efficient, completely data-based method to detect unstable periodic orbits (UPOs) in high-dimensional chaotic systems. For (1), we showed that anti-phase synchronization can emerge in a pair of electrically coupled micro-mechanical beams. Under impulsive perturbation, desynchronization occurs, distorting the output of each beam. We derived a formula for the relaxation rate and verified it numerically. We also found that the difference between the displacements of the two beams, or the differential signal, is robustly immune to impulsive perturbation, implying that the system can effectively counter external disturbances. This can have significant applications in developing various micro-scale devices, which we elaborated using MEM resonators. For (2), we addressed the fundamental question of whether multistability can arise in high-dimensional physical systems. Motivated by the ever increasing widespread use of nanoscale systems, we investigated a prototypical class of NEM systems: electrostatically driven Si-nanowires, mathematically described by a set of driven, nonlinear partial differential equations. We developed a computationally efficient algorithm to solve the equations, and found that multistability and complicated structures of basin of attraction are common types of dynamics, and the latter can be attributed to extensive transient chaos. We also explored implications of these phenomena to device operations. For (3), we developed a framework, integrating the approximation theory of neural networks and adaptive synchronization, to address the problem of time-series based detection of UPOs in high-dimensional chaotic systems.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2013
Accession Number
ADA586665

Entities

People

  • Ying-Cheng Lai

Organizations

  • Arizona State University

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Abstracts
  • Agreements
  • Boundaries
  • Chaos
  • Department Of Defense
  • Differential Equations
  • Dynamics
  • Engineering
  • Equations
  • Mathematics
  • Microelectromechanical Systems
  • Nanoelectromechanical Systems
  • Nanotechnology
  • Neural Networks
  • Nonlinear Dynamics
  • Partial Differential Equations
  • Students

Fields of Study

  • Physics

Readers

  • Fluid Dynamics.
  • Nanoscale Plasmonic Nanotechnology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Microelectronics
  • Microelectronics - Microelectromechanical Systems
  • Space