Effect of native oxide on stress in silicon nanowires: implications for nanoelectromechanical systems

Abstract

Understanding the origins of intrinsic stress in Si nanowires (NWs) is crucial for their successful utilization as transducer building blocks in next-generation, miniaturized sensors based on nanoelectromechanical systems (NEMS). With their small size leading to ultrahigh-resonance frequencies and extreme surface-to-volume ratios, silicon NWs raise new opportunities regarding sensitivity, precision, and speed in both physical and biochemical sensing. With silicon optoelectromechanical properties strongly dependent on the level of NW intrinsic stress, various studies have been devoted to the measurement of such stresses generated, for example, as a result of harsh fabrication processes. However, due to enormous NW surface area, even the native oxide that is conventionally considered as a benign surface condition can cause significant stresses. To address this issue, a combination of nanomechanical characterization and atomistic simulation approaches is developed. Relying only on low-temperature processes, the fabrication approach yields monolithic NWs with optimum boundary conditions, where NWs and support architecture are etched within the same silicon crystal. Resulting NWs are characterized by transmission electron microscopy and micro-Raman spectroscopy. The interpretation of results is carried out through molecular dynamics simulations with ReaxFF potential facilitating the incorporation of humidity and temperature, thereby providing a close replica of the actual oxidation environment-in contrast to previous dry oxidation or self-limiting thermal oxidation studies. As a result, consensus on significant intrinsic tensile stresses on the order of 100 MPa to 1 GPa was achieved as a function of NW critical dimension and aspect ratio. The understanding developed herein regarding the role of native oxide played in the generation of NW intrinsic stresses is important for the design and development of silicon-based NEMS.