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The atomic force microscopy (AFM) technology is a promising method for nanofabrication due to the high tunability of this affordable platform. The quality inspection and control significantly impact the manufacturing effectiveness for realizing the functionality of the achieved nanochannel. Particularly, the surface characteristics of nanochannel sidewalls, which play a significant role in determining the quality of the nanomachined products, can not be accurately captured using conventional surface integrity metrics (e.g., surface roughness). Therefore, it is necessary to propose a method to quantitatively characterize the surface morphology and detect the abnormal parts/regions of the nanochannel sidewall. This paper presents a statistical process control approach derived from the self-affine fractal model to detect the sidewall surface anomalies. It evaluates changes in the self-affine fractal model parameters (standard deviation, correlation length, and roughness exponent), which can be used to signify the changes on the sidewall surface; the statistical distributions of these parameters are derived and used to develop control charts to allow inspection of the sidewall morphology. The approach was tested on the AFM-based nanomachined samples. The results suggest that the presented approach can effectively reflect the abnormal regions on the machined parts, which opens up a new avenue toward guiding the quality control and rework for process improvement for AFM-based nanomachining. 

Atomic force microscope (AFM)-based nanomanufacturing offers an affordable and easily deployable method for fabricating high-resolution nanopatterns. This study employs a comprehensive design of experiment (DOE) approach to investigate the effects of various parameters, such as voltage, speed, and vibration axis, on the width and depth of lithography patterns using electrical field and vibration-assisted lithography on PEDOT: PSS films. The DOE explores the effect of voltage and speed on the process of electrical field and vibration-assisted AFM-based nanopatterning in two vibration trajectories: a circular trajectory employing X and Y axis vibration and a reciprocating trajectory employing Y axis vibration. The results indicate that using circular XY-vibration with a low stiffness contact probe and optimized speed and voltage factors results in higher depth and width of the lithography patterns compared to Y-vibration alone at the same parameters as expected. In both cases, pattern width was dominantly controlled by the voltage. Regarding depth, in XY-vibration, the speed of the tip is the most significant factor, while for Y-vibration, voltage plays the most significant role. It is noteworthy that there is a minimum threshold of speed that can produce a pattern; for example, the high-speed level that produced patterns in the circular trajectory (XY-vibration) did not produce patterns in reciprocating motion (Y-vibration). In conclusion, the study demonstrates the significant impact of voltage, speed, and axis on the width and depth of the lithography patterns. These findings can be instrumental in developing and understanding AFM-based high-resolution nanofabrication techniques. 

Vibration-assisted atomic force microscopy (AFM)-based nanomachining is a promising method for the fabrication of nanostructures. During mechanical nanomachining, the geometry of the tooltip and workpiece interface is sensitive to variations in the depth of cut, the material grain size, and system vibrations; understanding the underlying uncertainties is essential to improve the process capability. This paper investigates process uncertainties and their impacts on the achieved surface geometries based on an experimental study of AFM-based nanomachining. The variations and biases of the achieved surface characteristics (compared to the theoretical geometries) are observed and identified as the torsional deflections on the AFM probe. A physical-based model combined with the Kriging method is reported to capture such uncertainties and estimate the surface finish based on different process parameters.