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Updated: Jun 12, 2025

Fabrication of Silica Ultra High Quality Factor Microresonators
Published on: July 2, 2012
Unveiling the tradeoff between device scale and surface nonidealities for an optimized quality factor at room
Pengcheng Zhang1, Yueyang Jia1, Shuai Yuan1
1University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
Abstract:
A high quality (Q) factor is essential for enhancing the performance of resonant nanoelectromechanical systems (NEMS). NEMS resonators based on two-dimensional (2D) materials such as molybdenum disulfide (MoS2) have high frequency tunability, large dynamic range, and high sensitivity, yet room-temperature Q factors are typically less than 1000. Here, we systematically investigate the effects of device size and surface nonidealities on Q factor by measuring 52 dry-transferred fully clamped circular MoS2 NEMS resonators with diameters ranging from 1 μm to 8 μm, and optimize the Q factor by combining these effects with the strain-modulated dissipation model. We find that Q factor first increases and then decreases with diameter, with an optimized room-temperature Q factor up to 3315 ± 115 for a 2-μm-diameter device. Through extensive characterization and analysis using Raman spectroscopy, atomic force microscopy, and scanning electron microscopy, we demonstrate that surface nonidealities such as wrinkles, residues, and bubbles are especially significant for decreasing Q factor, especially for larger suspended membranes, while resonators with flat and smooth surfaces typically have larger Q factors. To further optimize Q factors, we measure and model Q factor dependence on the gate voltage, showing that smaller DC and radio-frequency (RF) driving voltages always lead to a higher Q factor, consistent with the strain-modulated dissipation model. This optimization of the Q factor delineates a straightforward and promising pathway for designing high-Q 2D NEMS resonators for ultrasensitive transducers, efficient RF communications, and low-power memory and computing.

