Adeniyi Kehinde Adeleke’s scientific contributions

Nanofabrication plays a crucial role in the advancement of nanotechnology, enabling the development of high-precision nanoscale devices. However, the complexity of nanofabrication processes often introduces various sources of noise that affect metrological measurements, leading to deviations from expected performance metrics. This study presents a comprehensive modeling approach to optimize nanofabrication processes while simultaneously implementing noise reduction strategies in metrological measurements. The research integrates analytical modeling, computational simulations, and experimental validation to enhance process efficiency and measurement accuracy. The study first examines key nanofabrication techniques, including electron beam lithography (EBL), nanoimprint lithography (NIL), and atomic layer deposition (ALD), identifying the primary sources of noise inherent in these processes. These noise factors include thermal fluctuations, mechanical vibrations, and quantum effects, which contribute to measurement inaccuracies. A mathematical framework is developed to quantify these noise sources and predict their impact on measurement precision. Computational modeling, leveraging finite element analysis (FEA) and machine learning algorithms, is used to optimize process parameters and mitigate noise-induced distortions. To improve metrological accuracy, advanced noise reduction strategies such as adaptive filtering, signal averaging, and machine learning-based noise suppression techniques are implemented. Experimental validation is conducted using scanning electron microscopy (SEM), atomic force microscopy (AFM), and X-ray photoelectron spectroscopy (XPS) to assess the effectiveness of the proposed strategies. The results demonstrate significant improvements in measurement precision, reducing error margins and enhancing reproducibility in nanofabrication processes. This research contributes to the field of nanomanufacturing by providing a systematic methodology for modeling and optimizing nanofabrication processes while minimizing noise-related metrological uncertainties. The findings have significant implications for semiconductor manufacturing, biomedical devices, and nanoscale electronics, where precise measurements are critical for device performance and reliability. DOI: https://doi

Ensuring the accuracy and safety of medical devices is paramount to guaranteeing their effectiveness in clinical applications. The integration of nanometrology and non-destructive testing (NDT) techniques has emerged as a critical approach for enhancing the precision, reliability, and regulatory compliance of medical device manufacturing. Nanometrology, the science of measurement at the nanoscale, enables the characterization of microstructural properties, surface topography, and dimensional accuracy with unprecedented precision. Meanwhile, non-destructive testing (NDT) methods, such as ultrasonic testing, X-ray computed tomography (XCT), and optical coherence tomography (OCT), offer real-time evaluation without compromising the structural integrity of medical components. This study explores advanced nanometrology techniques, including atomic force microscopy (AFM), scanning electron microscopy (SEM), and white light interferometry, for assessing surface roughness, dimensional tolerances, and coating uniformity in biomedical implants and devices. These techniques are crucial for verifying nanostructured surfaces, which are increasingly used to improve biocompatibility and antimicrobial properties. Furthermore, the implementation of NDT methods in medical device manufacturing ensures early defect detection, material integrity assessment, and process optimization. The adoption of advanced imaging and spectroscopic techniques, such as terahertz imaging and laser-induced breakdown spectroscopy (LIBS), enhances defect identification, layer thickness analysis, and

The increasing demand for high-precision manufacturing has necessitated the development of advanced material characterization techniques. Nanoindentation and non-contact optical metrology have emerged as powerful methods for evaluating the mechanical and surface properties of materials with high accuracy and resolution. Nanoindentation provides quantitative measurements of hardness, elastic modulus, and other mechanical properties at micro-and nanoscale levels. It utilizes an indenter to apply controlled force while recording depth penetration, offering insights into deformation behavior, phase transformations, and time-dependent mechanical responses such as creep and stress relaxation. However, traditional contact-based indentation techniques have limitations, including surface damage and measurement errors due to tip blunting. To overcome these challenges, non-contact optical metrology techniques, including interferometry, confocal microscopy, and white light profilometry, have been integrated with nanoindentation for enhanced material characterization. These optical methods allow for precise surface topography measurements without mechanical interaction, reducing the risk of surface alteration and increasing measurement repeatability. Additionally, advancements in machine learning and artificial intelligence have facilitated the automated analysis of nanoindentation and optical data, improving efficiency and accuracy in large-scale material assessments. The integration of nanoindentation with non-contact optical metrology provides a comprehensive approach to characterizing mechanical properties while simultaneously mapping surface roughness, texture, and defects. This combination is particularly beneficial in the semiconductor, aerospace, biomedical, and additive manufacturing industries, where surface integrity and mechanical performance are critical. By correlating indentation responses with optical surface measurements, a deeper understanding of material behavior can be achieved, leading to improved process control, quality assurance, and failure analysis in manufacturing. This study explores the latest developments in nanoindentation and optical metrology, focusing on their synergistic application for precise material characterization. Experimental case studies demonstrate how these techniques enhance measurement resolution, reproducibility, and sensitivity across various material systems. The findings provide valuable insights into optimizing metrology techniques for next-generation manufacturing applications.

The evolution of coordinate measuring machines (CMMs) has significantly improved precision in dimensional metrology, driven by the need for higher accuracy in manufacturing and quality assurance. Advanced numerical control (NC) systems play a pivotal role in optimizing CMM performance by enhancing motion control, reducing measurement uncertainty, and improving data acquisition speed. This research focuses on modeling advanced NC systems to enhance precision in next-generation CMMs by integrating artificial intelligence (AI)-driven control algorithms, real-time error compensation techniques, and adaptive feedback mechanisms. A hybrid modeling approach is proposed, combining physics-based dynamic modeling with AI-based predictive control to achieve sub-micron accuracy. The study explores the integration of real-time kinematic error compensation, leveraging machine learning algorithms to predict and correct deviations caused by thermal expansion, mechanical vibrations, and backlash effects. The model also incorporates sensor fusion techniques to improve the precision of spatial positioning, utilizing high-resolution encoders, laser interferometry, and inertial measurement units. Finite element analysis (FEA) is used to simulate the mechanical behavior of CMM structures under various loading conditions, ensuring optimal rigidity and stability. Additionally, a robust closed-loop control strategy employing proportional-integral-derivative (PID) controllers and fuzzy logic is implemented to enhance motion smoothness and reduce positional drift. The research further investigates the impact of advanced trajectory planning algorithms, such as jerk-limited motion profiles and model predictive control (MPC), in minimizing dynamic errors and improving measurement repeatability. Experimental validation is conducted on a prototype CMM equipped with an advanced NC system, demonstrating significant improvements in precision and repeatability compared to conventional systems. The results show that integrating AI-driven control, real-time error compensation, and adaptive feedback significantly reduces measurement errors and enhances system robustness. This study provides a comprehensive framework for developing next-generation CMMs with enhanced precision, paving the way for more accurate and reliable metrology solutions in aerospace, automotive, and semiconductor industries. Future work will focus on further optimizing AI-based control algorithms and exploring the potential of digital twin technology for real-time CMM performance monitoring and predictive maintenance.