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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. 

The vibration-assisted atomic force microscope (AFM)-based nanomachining offers a promising opportunity for low-cost nanofabrication with high tunability. However, critical challenges reside in advancing the throughput and the quality assurance of the process due to extensive offline experimental investigations and characterizations, which in turn hinders the wide industry applications of current AFM-based nanomachining process. Hence, it is necessary to create an in-process monitoring for the nanomachining to allow real-time inspection and process characterizations. This paper reports a sensor-based analytic approach to allow real-time estimations of the AFM-based nanomachining process. The temporal-spectral features of collected acoustic emission (AE) sensor signals are applied to predict the depth morphology of nanomachined trenches under different machining conditions. The experimental case study suggests that the most significant frequency domain information from AE sensor can accurately predict (R-squared value around 92%) the nanomachined depth profile. It, therefore, breaks the current limitation of characterization tools at the nanoscale precision level, and opens up an opportunity to allow real-time estimation for quality inspection of vibration-assisted AFM-based nanofabrication process. 

The atomic force microscope (AFM)-based nanomachining has the potential for highly customized nanofabrication due to its low cost and tunability. However, the low productivity and issues related to the quality assurance for AFM-based nanomachining impede it from large-scale production due to the extensive experimental study for turning process parameters with time-consuming offline characterizations. This work reports an analytic approach to capturing the AE spectral frequency responses from the nanopatterning process using vibration-assisted AFM-based nanomachining. The experimental case study suggests the presented approach allows characterizations of subtle variations on the AE frequency responses during the nanomachining processes (with overall 93% accuracy), which opens up the chance to explain the variations of the nano-dynamics using the senor-based monitoring approach for vibration-assisted AFM-based nanomachining. 

Abstract The recent COVID-19 pandemic reveals the vulnerability of global supply chains: the unforeseen supply crunches and unpredictable variability in customer demands lead to catastrophic disruption to production planning and management, causing wild swings in productivity for most manufacturing systems. Therefore, a smart and resilient manufacturing system (S&RMS) is promised to withstand such unexpected perturbations and adjust promptly to mitigate their impacts on the system’s stability. However, modeling the system’s resilience to the impacts of disruptive events has not been fully addressed. We investigate a generalized polynomial chaos (gPC) expansion-based discrete-event dynamic system (DEDS) model to capture uncertainties and irregularly disruptive events for manufacturing systems. The analytic approach allows a real-time optimization for production planning to mitigate the impacts of intermittent disruptive events (e.g., supply shortages) and enhance the system’s resilience. The case study on a hybrid bearing manufacturing workshop suggests that the proposed approach allows a timely intervention in production planning to significantly reduce the downtime (around one-fifth of the downtime compared to the one without controls) while guaranteeing maximum productivity under the system perturbations and uncertainties. 

Abstract In this study, we carry out robust optimal design for the machining operations, one key process in wafer polishing in chip manufacturing, aiming to avoid the peculiar regenerative chatter and maximize the material removal rate (MRR) considering the inherent material and process uncertainty. More specifically, we characterize the cutting tool dynamics using a delay differential equation (DDE) and enlist the temporal finite element method (TFEM) to derive its approximate solution and stability index given process settings or design variables. To further quantify the inherent uncertainty, replications of TFEM under different realizations of random uncontrollable variables are performed, which however incurs extra computational burden. To eschew the deployment of such a crude Monte Carlo (MC) approach at each design setting, we integrate the stochastic TFEM with a stochastic surrogate model, stochastic kriging, in an active learning framework to sequentially approximate the stability boundary. The numerical result suggests that the nominal stability boundary attained from this method is on par with that from the crude MC, but only demands a fraction of the computational overhead. To further ensure the robustness of process stability, we adopt another surrogate, the Gaussian process, to predict the variance of the stability index at unexplored design points and identify the robust stability boundary per the conditional value at risk (CVaR) criterion. Therefrom, an optimal design in the robust stable region that maximizes the MRR can be identified.