An Integrated Nanocomposite Proximity Sensor: Machine Learning-Based Optimization, Simulation, and Experiment

Abstract

This paper utilizes multi-objective optimization for efficient fabrication of a novel Carbon Nanotube (CNT) based nanocomposite proximity sensor. A previously developed model is utilized to generate a large data set required for optimization which included dimensions of the film sensor, applied excitation frequency, medium permittivity, and resistivity of sensor dielectric, to maximize sensor sensitivity and minimize the cost of the material used. To decrease the runtime of the original model, an artificial neural network (ANN) is implemented by generating a one-thousand samples data set to create and train a black-box model. This model is used as the fitness function of a genetic algorithm (GA) model for dual-objective optimization. We also represented the 2D Pareto Frontier of optimum solutions and scatters of distribution. A parametric study is also performed to discern the effects of the various device parameters. The results provide a wide range of geometrical data leading to the maximum sensitivity at the minimum cost of conductive nanoparticles. The innovative contribution of this research is the combination of GA and ANN, which results in a fast and accurate optimization scheme.

Recent Progress in Micro- and Nanotechnology-Enabled Sensors for Biomedical and Environmental Challenges

“…These devices have enabled the high-throughput screening of drug candidates and the characterization of their biological activities [ 4 , 36 , 37 , 38 , 39 , 40 , 41 ]. Additionally, the integration of sensors with emerging technologies such as artificial intelligence and machine learning has further expanded their potential applications in the biomedical field [ 10 , 42 ].…”

Solution Properties of a New Dynamic Model for MEMS with Parallel Plates in the Presence of Fringing Field

“…Among them, the capacitive fringing field is one of the most frequent causes of instability of electrostatic MEMS devices [39,40]. As is known, it depends on the geometry of the device (in particular on its length/width ratio, L/d) producing in the device harmful effects on the bending of the lines of force of E [41,42], and it is important to highlight that this influence is stronger near the edges, while it is considered to be negligible in the center [43][44][45][46].…”