arXiv subjects

Ahmed Othman

Publications and source records attributed to Ahmed Othman.

5 recordsLinked to original sources

arXiv2026

Enhancing Conformality in Atomic Layer Deposition through Low Growth Per Cycle

Atomic layer deposition (ALD) is a key enabling technology for advanced microelectronics as it enables the growth of functional thin films on complex three-dimensional substrates with unmatched atomic-scale precision. Conformal film growth in high-aspect-ratio (HAR) structures is limited by slow diffusion of reactant molecules into deep, narrow features. This work elucidates an approach to grow conformal films faster by investigating the relationship between the ALD growth per cycle (GPC) and the film penetration depth in HAR structures through modeling and experiments. Diffusion-reaction simulations reveal, under Knudsen diffusion conditions, an inverse square root relationship between the film penetration depth and GPC. The prediction is validated experimentally with a model zinc oxide ALD process on rectangular lateral HAR structures, using an inhibitor molecule to decrease the GPC. While ALD is traditionally optimized for "high GPC," this work shows that "low GPC" may increase efficiency when conformality is key.

cond-mat.mtrl-sci↗

arXiv2025

Regulating radiology AI medical devices that evolve in their lifecycle

Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease manifestations. While human radiologists can adjust their expertise to accommodate such variations, deep learning models cannot. In fact, such models are highly susceptible to even slight variations in image characteristics. Consequently, manufacturers must conduct regular updates to ensure that they remain safe and effective. Performing such updates in the United States and European Union required, until recently, obtaining re-approval. Given the time and financial burdens associated with these processes, updates were infrequent, and obsolete systems remained in operation for too long. During 2024, several regulatory developments promised to streamline the safe rollout of model updates: The European Artificial Intelligence Act came into effect last August, and the Food and Drug Administration (FDA) issued final marketing submission recommendations for a Predetermined Change Control Plan (PCCP) in December. We provide an overview of these developments and outline the key building blocks necessary for successfully deploying dynamic systems. At the heart of these regulations - and as prerequisites for manufacturers to conduct model updates without re-approval - are clear descriptions of data collection and re-training processes, coupled with robust real-world quality monitoring mechanisms.

cs.CY↗

arXiv2024

Feather-inspired flow control: The flow physics of spatially distributed covert flaps

This study presents a novel spatially disrupted flow control system inspired by the covert feathers on bird wings. The system is a passive flow control system consisting of multiple feather-inspired flaps that dynamically interact with the surrounding flow to mitigate stall. Incorporating covert-inspired flaps on the suction side of the airfoil resulted in a substantial increase in lift (up to 50%) and a substantial reduction in drag (up to 30%) in post-stall conditions. Using wind tunnel experiments and time-resolved particle image velocimetry, the physical mechanisms responsible for post-stall lift improvements and drag reduction are identified as (1) shear layer interaction and (2) pressure dam effect. In the first mechanism, flap deployment reduces the geometric adverse pressure gradient that the flow encounters, reducing the degree of flow separation. In the second mechanism, the deployed flap acts as a barrier, preventing the relatively high pressure downstream from propagating upstream of the airfoil. The flow control mechanism employed was a function of the location of the flap. Flaps near the leading edge interacted mainly with the shear layer, while flaps near the trailing edge induced a pressure dam effect. Increasing the number of flaps along the chord increased the gain in lift and the reduction in drag. However, additive performance enhancements were sensitive to spatial distribution and flow control mechanisms. The shear layer interaction mechanism is found to be additive; that is, the deployment of additional flaps increases the lift gain, whereas the pressure dam effect is not.

physics.flu-dyn↗

arXiv2022

Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts

Most continual learning methods are validated in settings where task boundaries are clearly defined and task identity information is available during training and testing. We explore how such methods perform in a task-agnostic setting that more closely resembles dynamic clinical environments with gradual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validation on two scenarios of hippocampus segmentation shows that our proposed method reliably maintains performance on earlier tasks without losing plasticity.

cs.CV↗

arXiv2015

Evolving Fuzzy Image Segmentation with Self-Configuration

Current image segmentation techniques usually require that the user tune several parameters in order to obtain maximum segmentation accuracy, a computationally inefficient approach, especially when a large number of images must be processed sequentially in daily practice. The use of evolving fuzzy systems for designing a method that automatically adjusts parameters to segment medical images according to the quality expectation of expert users has been proposed recently (Evolving fuzzy image segmentation EFIS). However, EFIS suffers from a few limitations when used in practice mainly due to some fixed parameters. For instance, EFIS depends on auto-detection of the object of interest for feature calculation, a task that is highly application-dependent. This shortcoming limits the applicability of EFIS, which was proposed with the ultimate goal of offering a generic but adjustable segmentation scheme. In this paper, a new version of EFIS is proposed to overcome these limitations. The new EFIS, called self-configuring EFIS (SC-EFIS), uses available training data to self-estimate the parameters that are fixed in EFIS. As well, the proposed SC-EFIS relies on a feature selection process that does not require auto-detection of an ROI. The proposed SC-EFIS was evaluated using the same segmentation algorithms and the same dataset as for EFIS. The results show that SC-EFIS can provide the same results as EFIS but with a higher level of automation.

cs.CV↗