Elena Koleva

Electron beam lithography (EBL) using bilayer resists is influenced by electron scattering, proximity effects, resist thickness, pattern density, and exposure dose. Optimization based only on the top-view critical dimension does not capture linewidth variation through the developed resist thickness. This study presents an experimental and theoretical approach to model and optimize the depth-dependent sidewall geometry of bilayer PMMA exposed by a variable-shaped-beam EBL at 20 keV. Bilayers consisting of 950 PMMA A2 and 495 PMMA A6, with total thicknesses of 300 and 550 nm, on SiO₂/Si substrates were fabricated. We investigated isolated lines and line gratings with nominal line/space (L/S) dimensions of 100/500 and 100/700 nm over selected exposure dose ranges. Cross-sectional SEM measurements at five resist depths were used to develop regression models describing linewidth as a function of the exposure dose and resist depth. Two models for mean values and standard deviations were constructed for each of the considered experimental cases to account for external noise not included in the regression models, and the propagated uncertainties are quantified by Monte Carlo simulation. Four linewidth-difference criteria to identify doses producing near-vertical profile segments were introduced and applied. The results demonstrate that the selected optimum dose depends strongly on the bottom-layer thickness, pattern density, and the profile segment considered. The proposed approach provides a quantitative empirical method for predicting, analyzing, controlling, and optimizing EBL resist profiles, and can also be applied, with appropriate experimental calibration, to different resist systems, pattern geometries, exposure dose ranges, beam shot sizes, and acceleration voltages.

Declining water availability, eutrophication and increasing anthropogenic pressure on aquatic ecosystems highlight the need for sustainable approaches for nutrient reduction and safe water management. This study evaluates a pilot, multi-stage filtration system as a possible additional step for the further treatment of water. Two experimental filtration units were tested with water from Burgas Lake (Vaya Lake), as the lake is highly anthropogenically polluted: a filter containing natural zeolite (clinoptilolite) and a filter with biomass from the macrophyte Stuckenia pectinata. After 60 minutes of recirculation, a reduction of TN (Total Nitrogen) to 67-76%, of N-NO₃ (Nitrogen Nitrate) to 19-38% and of PO₄³- P (Orthophosphate) to 58-66% was observed. The results show that both nature-based filtration media effectively reduce nitrogen and phosphorus compounds under controlled laboratory conditions, with a higher nutrient removal efficiency observed for the Stuckenia pectinata filter. The proposed pilot system can serve as a preliminary modelling framework for further treatment of anthropogenically polluted surface waters and for future development of risk-based water management approaches.

This study presents the development of a framework for robust engineering design in Electron Beam Additive Manufacturing, aiming to ensure product quality and reliability. It proposes a methodology, based on models, linking process parameters, equipment characteristics, and product quality attributes for additive manufacturing during process design, development, and optimization. By analysing interactions between equipment-specific factors and product quality, the study establishes a framework for improving process control and reducing variability. The research is directed toward the supporting the development of standardized protocols for consistent quality in high-performance applications, addressing challenges in additive manufacturing like process variability. The results are particularly relevant for industries requiring precision and reliability, such as aerospace, medical devices, and energy systems.

Data mining techniques have become indispensable in automating defect detection and classification in industrial welding processes, particularly through the use of image-based data. This study investigates the application of data mining methodologies for detecting and classifying weld defects using photographic datasets processed with the Orange data mining software. By leveraging Orange's visual programming interface, the research demonstrates how image data can be analyzed and modeled to identify welding defects. Key machine learning techniques, including Artificial Convolutional Neural Networks, Logistic Regression, Random Forest, k-Nearest Neighbors for image classification, were applied to achieve high accuracy in defect recognition. Special emphasis was placed on image preprocessing and feature extraction to enhance model performance. The results confirm that Orange offers an intuitive platform for integrating image-based data into sophisticated machine learning workflows, enabling accurate and interpretable classification outcomes. This approach highlights the potential of combining image data with domain-specific software to optimize defect detection processes and improve manufacturing quality.

Water plays a fundamental role in sustaining biological processes, ecological functions, and economic systems. However, the progressive pollution of water sources compromises these functions, posing significant threats to water purity, human well-being, and environmental sustainability. Human activities, such as industrial waste, agriculture, and urbanization, alongside natural processes, are major contributors to the deterioration of surface water quality, which in turn leads to environmental and economic risks. The decline in water quality results in issues such as waterborne diseases, loss of biodiversity, and a shortage of clean water for consumption and industrial use. This paper emphasizes the critical need for maintaining good water quality and the importance of implementing effective strategies for the removal of physical, chemical, and biological contaminants. In response, this work presents an intelligent embedded system (electronic control unit, ECU) developed as part of a modular filtration system designed to improve surface water quality, provide more precise water analyses, and perform tests within a controlled environment.

Arduino-based sensor systems are gaining widespread adoption in modern technological applications due to their accessibility, low-cost components, diverse sensor compatibility, high reliability, and user-friendly programming. Because of these advantages, such a system was selected to monitor and control microclimate parameters in a small-scale experimental greenhouse. The greenhouse will cultivate several vegetable species in soils with varying zeolite concentrations. The aim of this paper is to present the design and prototype development of a sensor system capable of tracking key environmental parameters, including temperature, humidity, atmospheric pressure, and soil moisture, while also enabling automated irrigation.

The model-based robust approach is successfully applied to different industrial processes for improving their quality performance characteristics. The Robust engineering approach is implemented in cases of heteroscedasticity of the performance characteristics in presence of errors in the process parameters or noise factors, which is typical for the production processes. Higher quality and reproducibility of the quality characteristics can be achieved both for existing production processes or for technological process design at the production planning and development stages. In this paper the model-based robust engineering approach is implemented for the quality improvement of polymethyl methacrylate (PMMA) resist profiles, obtained by electron beam lithography process. Specific technological requirements concerning the developed profile structures, their variances (reproducibility) in production conditions and robustness toward the errors in the process parameters, are considered. Different multicriteria optimization approaches were applying and compared, aiming to simultaneously fulfil the requirements for the geometric characteristics of the positive resist PMMA.

The application of electron beam sweep makes it possible to carry out multifocal and multi-beam welding, as well as combine the welding process with local heating or subsequent heat treatment, which is important when preparing products from thermally-hardened materials. This paper presents a method of electron beam welding (EBW) with dynamic beam positioning and its experimental-calculation results regarding the formation of structures and properties of heat-resistant steel welded joints (grade of steel 20Cr3MoWV). The application of electron beam oscillations in welding makes it possible to change the shape and dimensions of welding pool. It also affects the crystallization and formation of a primary structure. It has been established that EBW with dynamic beam positioning increases the weld metal residence time and the thermal effect zone above the critical A₃ point, increases cooling time and considerably reduces instantaneous cooling rates as compared to welding without beam sweep. Also, the difference between cooling rates in the depth of a welded joint considerably reduces the degree of structural non-uniformity. A bainitic–martensitic structure is formed in the weld metal and the thermal effect zone throughout the whole depth of fusion. As a result of this structure, the level of mechanical properties of a welded joint produced from EBW with dynamic electron beam positioning approaches that of parent metal to a greater extent than in the case of welding by a static beam. As a consequence, welding of heat-resistant steels reduces the degree of non-uniformity of mechanical properties in the depth of welded joints, as well as decreases the level of hardening of a welded joint in relation to parent metal.

The electron beam processes – 3D selective melting (3D printing), welding, etc. are complex processes with many processing variables, which require integration of interfaces for control of the processes on different levels through hierarchical equipment models, functional data and operation control models. We developed a user interface software application for controlling the electron-beam motion along several types of trajectories and a free complex shape trajectory by controlling the deflection system parameters. Several types of motion patterns with different velocities are realized, together with the possibility of choosing a free trajectory by defining the electron-beam path by a graphical image.

A time-dependent heat model is implemented for the simulation of the heat distribution in 316L stainless steel samples during electron-beam irradiation. The influence is studied of the electro-beam power and processing time on the temperature field produced in the samples. The simulation results are compared with real experimental data of electron-beam surface modification of 0.5-mm-thick 316L stainless steel samples using the ELIT-60 installation in the Institute of Electronics BAS. The simulation and the experimental results show good agreement.