Monocrystalline Silicon Research Articles

Nanoindentation-induced interface behavior between DLC film and monocrystalline silicon substrate at high temperature: A molecular dynamics simulation study

Diamond-like carbon (DLC) film is an excellent protective coating material, which has a wide range of applications. The interface behavior between around 1 nm DLC film and the monocrystalline silicon substrate is difficult to characterize through experiments. In this work, the nanoindentation molecular dynamics simulations are performed to investigate the interface behavior between DLC film and the silicon substrate at high temperature. A method for determining the depth of indentation has been proposed. As the temperature increases, the maximum repulsive force between indenter and DLC film decreases, the maximum adhesive force increases. High atomic shear strains locate at the interface between DLC and monocrystalline silicon during indentation process. High temperature enlarges the atomic strain distribution region. The number of holes in the monocrystalline silicon substrate increases with increase of temperature after indentation process. The evolution processes of silicon atomic structures have been revealed. Both the repulsive force and adhesive force in the indentation process contribute the silicon phase transformation. But the final silicon atom bonding structure and pop-out phenomena are determined by the adhesive force during the unloading process.

A Review of Emerging Technologies in Ultra-Smooth Surface Processing for Optical Components.

Advancements in astronomical telescopes and cutting-edge technologies, including deep ultraviolet (DUV) and extreme ultraviolet (EUV) lithography, have escalated demands and imposed stringent surface quality requirements on optical system components. Achieving near-ideal optical components requires ultra-smooth surfaces with sub-nanometer roughness, no sub-surface damage, minimal surface defects, low residual stresses, and intact lattice integrity. This necessity has driven the rapid development and diversification of ultra-smooth surface fabrication technologies. This paper summarizes recent advances in ultra-smooth surface processing technologies, categorized by their material removal mechanisms. A subsequent comparative analysis evaluates the roughness and polishing characteristics of ultra-smooth surfaces processed on various materials, including fused silica, monocrystalline silicon, silicon carbide, and sapphire. To maximize each process's advantages and achieve higher-quality surfaces, the paper discusses tailored processing methods and iterations for different materials. Finally, the paper anticipates future development trends in response to current challenges in ultra-smooth surface processing technology, providing a systematic reference for the study of the production of large-sized freeform surfaces.

Assessment of the Performance and Defect Investigation of PV Modules after Accelerated Ageing Tests

Photovoltaic (PV) modules' degradation behaviour, together with outdoor field condition and fault diagnostics, consist valuable data in evaluating efficiency and establishing long-term reliability of a PV system. However, since PV modules commonly come with 25 (even over) years of warranty, testing modules in field for that long period is not feasible. Thus, due to time constraints, many accelerated aging tests have been recently introduced for testing reliability and durability of PV modules. Scope of the presented study is the assessment of the performance degradation and fault (defect) propagation mechanisms with regard to three monocrystalline silicon (mc-Si) PV modules, through a specific thermal cycle accelerated ageing test. The parameters that were experimentally evaluated towards the accelerated ageing process of this study, were: i) the thermal signature evolution and/or propagation of any diagnosed defects, especially in two modules, by means of infrared (IR) thermographic measurements, ii) the overall performance degradation of each module, by means of electrical I-V measurements, together with iii) specific morphological characteristics, by means microscopy imaging. The intended study gave useful and promising results in assessing the effect of the applied thermal cycles upon the modules’ thermal behaviour, electrical efficiency and morphology.

Prediction of Subsurface Microcrack Damage Depth Based on Surface Roughness in Diamond Wire Sawing of Monocrystalline Silicon.

In diamond wire saw cutting monocrystalline silicon (mono-Si), the material brittleness removal can cause microcrack damage in the subsurface of the as-sawn silicon wafer, which has a significant impact on the mechanical properties and subsequent processing steps of the wafers. In order to quickly and non-destructively obtain the subsurface microcrack damage depth (SSD) of as-sawn silicon wafers, this paper conducted research on the SSD prediction model for diamond wire saw cutting of mono-Si, and established the relationship between the SSD and the as-sawn surface roughness value (SR) by comprehensively considering the effect of tangential force and the influence of the elastic stress field and residual stress field below the abrasive on the propagation of median cracks. Furthermore, the theoretical relationship model between SR and SSD has been improved by adding a coefficient considering the influence of material ductile regime removal on SR values based on experiments sawing mono-Si along the (111) crystal plane, making the theoretical prediction value of SSD more accurate. The research results indicate that a decrease in wire speed and an increase in feed speed result in an increase in SR and SSD in silicon wafers. There is a non-linear increasing relationship between silicon wafer SSD and SR, with SSD = 21.179 Ra4/3. The larger the SR, the deeper the SSD, and the smaller the relative error of SSD between the theoretical predicted and experimental measurements. The research results provide a theoretical and experimental basis for predicting silicon wafer SSD in diamond wire sawing and optimizing the process.

Performance and degradation assessment of two different solar PV cell technologies in the remote region of eastern India

The sole reliance on conventional energy sources proves inadequate to meet the surging energy demand. To address this challenge, a self-sustainable hybrid system based on solar PV emerges as a promising solution. In light of this issue, it becomes important to analyze the performance of a solar PV array. The study aims to assess the performance and degradation of two distinct solar PV technologies, polycrystalline silicon PV (Poly-Si PV) and monocrystalline silicon PV (Mono-Si PV), under the tropical climatic conditions of eastern India. Various performance indices of solar PV arrays, such as reference yield, array yield, final yield, array capture loss, capacity factor (CF) and performance ratio (PR) for the two PV technologies, have been meticulously calculated to analyze the performance of solar PV arrays. The Linear regression (LR) method is employed to assess the degradation rate of both technologies. The results show better performance for Mono-Si PV in terms of yield outcomes and losses compared to its Poly-Si PV counterpart. The yield outcome of Mono-Si PV is approximately 12 % higher, whereas losses are around 32 % lower than Poly-Si PV. The annual average PR for Mono-Si PV and Poly-Si PV is obtained as 77.5 % and 72.42 %, respectively. Correspondingly, the average yearly CF is 16.78 % for Mono-Si PV and 15.44 % for Poly-Si PV. Results also show that Poly-Si PV degrades faster as compared to Mono-Si PV, with an annual average degradation rate of 0.67 % for Mono-Si PV and 0.73 % for Poly-Si PV. Finally, economic analysis of the two systems reveals that Mono-Si PV provides energy at a 9 % lower cost than Poly-Si PV, with a levelized cost of energy (LCOE) of 0.248 $/kWh for Mono-Si PV and 0.273 $/kWh for Poly-Si PV.

Research on the Influence of Calcination Temperature on the Content of Free Silicon Dioxide in Dust

In order to verify the oxidation of silicon to silicon dioxide during the calcination process of silica containing dust, silicon powder and monocrystalline silicon were calcined at different temperatures. The sample was analyzed using Fourier transform infrared spectroscopy (FTIR) and compared with the FTIR spectrum of the silica standard. The results show that the antisymmetrical stretching vibration, symmetrical stretching vibration and bending vibration absorption peak of Si-O-Si were obviously in the FTIR spectra of silicon powder and monocrystalline silicon calcined at 750oC. The positions of those absorption peaks correlate broadly with the absorption peaks of standard silica. It is concluded that when the calcination temperature was above 750oC, elemental silicon would be oxidized to silica at different levels, which would cause a higher silica content detected in dust.

Texturization of Silicon Wafers for Solar Cells by Anisotropic Etching with Sodium Silicate Solutions

The efficiency of silicon solar cells greatly depends on the texturization of silicon wafers used in their fabrication. Texturization reduces the reflectance of the silicon surface which improves the light trapping ability of the solar cells. In this study, aqueous Na2SiO3 solutions were used as etchants in the texturization of monocrystalline silicon wafers. The main objective was to evaluate the performance of the etchant based on the silicon surface reflectance. The surface morphology and etching rate were also investigated. Results showed that Na2SiO3 solutions reduced the surface reflectance of the wafers better than other alkaline etchants. This was brought about by the formation of uniformly distributed pyramidal structures on the surface of the wafer after texturization. The effects of process parameters such as Na2SiO3 concentration, solution temperature and etching time on the reflectance were evaluated and optimized by applying ANOVA and Response Surface Method (RSM). The optimum process condition was found to be 6.2 wt% Na2SiO3, 80°C and 5 min at which the reflectance of the silicon wafer was reduced to 9.27%. Furthermore, the addition of small amounts of IPA (up to 0.03 wt%) to the Na2SiO3 solution reduced the reflectance of the wafer (9.13%) and slightly improved its wettability

Three-Dimensional Numerical Simulation of Rear Point Contact Crystalline Silicon Solar Cells

High efficiency monocrystalline solar cells commonly adopts rear point contacts of limited extension and passivation of the uncontacted bottom silicon surface region in order to improve performance. Modeling and analysis of advanced solar cells is strategic to optimize the device design and to minimize the losses. Several competing physical mechanisms must be accounted for in order to properly analyze rear point contact solar cells and a three-dimensional (3D) analysis of complex geometries is required. In this work we describe the issues related to the design of the mesh grid for the 3D numerical simulations, the definition of appropriate boundary conditions and the main adopted assumptions. Examples of numerical simulations are reported.

Investigation and optimisation of a lithium-drift silicon detector using Si–Li structure and bidirectional diffusion and drift techniques

Abstract The research relevance is predefined by the continuous development and improvement of radiation analysis methods and the need for more efficient and accurate detectors for various applications. This research may improve the sensitivity and resolution of Si(Li) detectors, which is important for scientific and industrial research as well as radiation safety monitoring. The research aims to analyse and improve the performance of a Si(Li) lithium-drift silicon detector. The methods used include an analytical method, classification method, functional method, statistical method, synthesis method and others. The results of the two-sided observation of lithium diffusion in silicon monocrystals provided valuable information about the characteristics of the process and its dependence on the method of silicon production. A large-diameter detector detection mode was found to be important for optimising the production of such detectors. The diffusion process in monocrystalline silicon produced by the shadowless zone melting method is relatively fast. This means that lithium ions penetrate the material rapidly and spread evenly throughout its volume. This fast diffusion process can be useful for detectors that need to respond quickly to incoming signals. It was found that in monocrystalline silicon produced by the Czochralski method, there is a delayed penetration of lithium ions.

Dislocation determination and quality control of industrial casting monocrystalline silicon

By analyzing dislocation ratio data of casting monocrystalline silicon wafers and photovoltaic conversion efficiency of the fabricated solar cells and comparing analyzing growth height and dislocation ratio of silicon crystalline, we derived the calculation equation of dislocation growth in the vertical direction. According to this, the determination software for the dislocation area of casting monocrystalline silicon blocks has been developed, and we realized the adequate delineation and interception of serious dislocation area in casting monocrystalline silicon blocks. The batch conversion efficiency gap between the casting monocrystalline silicon and the Czochralski (CZ) monocrystalline silicon has been narrowed from 0.51 % to 0.19 %, and the low efficiency ratio of casting monocrystalline silicon cells has been reduced from 6.98 % to 0 %. Finally, the stability and competitiveness of casting monocrystalline silicon wafer products have been improved.

Effect of rapid thermal annealing on the surface properties of the microlens arrays machined on monocrystalline silicon

Microstructure arrays, such as microlens arrays, play a significant role in optical systems, biomedical imaging, and telecommunications. However, the machining-induced subsurface defects deteriorate their performance and functionality. To address this issue, this study proposes a rapid thermal annealing method to heal the subsurface defects using different temperatures ranging from 200 °C to 1000 °C in a vacuum. Firstly, the ultra-precision diamond sculpturing process is employed to machine the high-quality microlens array on the monocrystalline silicon surface. Then, the effects of the rapid thermal annealing on the microlens array surface, such as surface roughness and subsurface defect, are comprehensively investigated. Experimental results show that the annealing temperature of >600 °C contributes to the decrease in surface roughness and subsurface defect. Furthermore, to reveal the healing mechanism, a molecular dynamics simulation of rapid thermal annealing is performed by controlling the bulk temperature of machined monocrystalline silicon from 500 K to 1300 K. The simulation results show that recrystallization always occurred on the interface between the amorphous layer and monocrystalline silicon substrate and the healing mechanism should be solid phase epitaxy rather than random nucleation. The research findings would contribute to fabricating the defect-free optical lens in the industry.

Evaporation and wetting behavior of water on the laser-textured silicon substrate

It is important to elucidate the impact of surface micromorphology on the evaporation mechanism and behavior to design rational micromorphologies for enhancing/regulating evaporation. In this study, we investigated the wetting and evaporation behaviors of water droplets on monocrystalline silicon surfaces with concentric circular textures etched using a laser engraving system at varying spacing intervals. Only when the texture spacing overlapped did it exhibit a significantly increased apparent surface energy and evaporation rate. The droplet volume decreased consistently at a constant evaporation rate at different temperatures, and the process involved two stages: constant contact radius (CCR) and constant contact angle (CCA). Laser etching increases the thickness of the oxide film from 10 to 377 nm, resulting in enhanced hydrophilicity. For millimeter-sized droplets, microscopic capillary phenomena in the vicinity of the triple line dominated the evaporation behavior rather than liquid/gas interface evaporation.

Wafer Defect Identification with Optimal Hyper-Parameter Tuning of Support Vector Machine Using the Deep Feature of ResNet 101

As semiconductor processing technologies continue to advance, semiconductor wafers are becoming more densely packed and intricate, resulting in a higher incidence of surface imperfections. Therefore, it is crucial to detect these defects early and accurately classify them to pinpoint the root causes of the defects in the manufacturing process, ultimately leading to improved yield. Therefore, defect detection is critical in the industrial production of monocrystalline silicon. This study employs deep learning techniques to propose a framework for detecting defects on silicon wafers, focusing on optimizing the hyperparameters of support vector machines (SVM). Three methods were utilized to fine-tune the SVM parameters: Bayesian optimization, grid search, and random search techniques. This study demonstrates how selecting optimal values for SVM parameters can lead to better classification. Additionally, real manufacturing data were utilized to evaluate the performance of the proposed SVM classifier, with a comparison to state-of-the-art techniques in the field. By using deep features from ResNet 101 and a support vector machine, this work achieves 74.5% accuracy in identifying wafer defects without employing any optimization technique. However, the performance of the model was further improved by utilizing the random search optimization technique, which yielded the best result among the three optimization techniques tested, with an accuracy of 88.1%.

An explainable artificial intelligence based approach for the prediction of key performance indicators for 1 megawatt solar plant under local steppe climate conditions

Motivated by the growing significance of solar energy, the paper presents the findings of the maiden study on the long-term seasonal performance assessment of three PV module technologies—monocrystalline (m-Si), polycrystalline (p-Si), and amorphous silicon (a-Si) housed in a 1 MW solar plant installed at a local steppe climate in Gandhinagar, Gujarat, India using the key performance indicators (KPIs), daily power generation, final yield (Yf), reference yield (Yr), total energy loss (TEL) and performance ratio (PR). Notably, the am-Si PV modules were found to be the best performing technology, with an average PR of 71.26% and the lowest average total energy loss of 631 h during the monitored period. Moreover, PV systems have become more popular as a source of green, clean energy; yet, they have low base-load energy sustainability. Explainable AI (XAI) aids in understanding predictions made by the machine learning models. The importance of XAI lies in the comprehension and trust which is embedded into the results of machine learning algorithms. Therefore, the present work builds a comprehensive XAI model for predicting KPIs and assesses the performance of several learning algorithms using R-value, MAE, the number of iterations, and execution time. The Levenberg-Marquardt algorithm predicts the KPIs for p-Si, am-Si, and m-Si with prediction accuracies of 98.63%, 98.58%, and 90.09%, respectively. In summary, this study not only advances our understanding of PV module performance in a steppe climate context but also contributes to the broader field by integrating an XAI model for transparent and reliable predictions of the KPIs.

Investigation of Coatings Formed by Thermal Oxidation on Monocrystalline Silicon

The results of complex experimental studies of the parameters of thin coatings of SiO2 oxides on Si, formed by thermal oxidation at temperatures of 1200 °C, including studies using electron and X-ray spectroscopy, IR-Fourier spectrometer IRTracer-100 - developed by Shimadzu, Raman scattering, and test electrophysical structures, are presented. Data have been obtained that for a silicon dioxide thickness of about 400 nm, the porosity of the coating does not exceed 2/cm2, and the electric field strength for the breakdown of the dioxide reaches 5·106 V/cm. The crystal structure of SiO2 is triclinic, lattice period a = 4.9160 Å, b = 4.9170 Å, c = 5.4070 Å, a = 90.000, b = 90.000, γ = 120.000, density 2.64 g/cm³.

Modeling and Simulation of Heterojunction Solar Cell with Mono Crystalline Silicon

Modeling and Simulation of Heterojunction Solar Cell with Mono Crystalline Silicon

Assessing the potential of TOPCon solar cells architecture using industrial n-type cast-mono silicon material

Cast-mono silicon material is interesting for its lower carbon footprint compared to Czochralski (Cz) monocrystalline silicon. However, solar cells fabricated using cast-mono (CM) silicon show lower performances. In this work, two routes to make cast-mono silicon advantageous over Cz silicon are considered. The first route is to further reduce carbon footprint of cast-mono silicon, by using Upgraded Metallurgical Grade silicon (UMG-Si) feedstock instead of Solar Grade silicon (SoG-Si) feedstock. TOPCon solar cells are fabricated using both feedstocks, and cast-mono growth technology, using industrial-type furnaces. Laboratory studies show that UMG-Si can result in efficiencies higher than solar cells made of SoG-Si when feeding the material to a CM crystallization process. But when compared to Cz, CM-UMG-Si TOPCon solar cells conversion efficiency values are still 0.5%abs lower. The second route is to take advantage of the TOPCon passivation layer (e.g., poly-Si) ability to getter metallic impurities, and thus improve the quality of cast-mono material. Several TOPCon sequences are tested and their effect on the carrier recombination properties of the device are studied. In the end, solar cells are fabricated and again, UMG-Si solar cells show better results than SoG-Si solar cells, with efficiency up to 22.65%, independently confirmed.

Research Collaboration of ITENAS Bandung – Indonesia and MATE Godollo – Hungary on the Photovoltaic Thematic Field: Achievements and Future Plan

Since the agreement was signed officially in 2013, Institut Teknologi Nasional Bandung (ITENAS Bandung) – Indonesia and Hungarian University of Agriculture and Life Sciences (MATE Godollo) – Hungary have implemented a lot of scholarly activities, and one of them is research collaboration in the field of solar energy, especially in the thematic field of photovoltaic (PV). A set of experimental facilities has been developed and constructed to support related research. Basic facilities to understand the principle of electricity generated by PV, i.e., having the solar power plant (SPP) laboratory scale have been fulfilled by both universities. MATE Godollo has a 10 kWp SPP installation and uses polycrystalline and amorphous silicon PV modules, meanwhile, ITENAS Bandung has 1 kWp SPP and uses monocrystalline silicon PV modules. In this paper, a set of activities related to PV research collaboration between ITENAS Bandung and MATE Godollo will be elaborated, including the output and the outcome of the activities, and plan activities involving other university partners, Slovak University of Agriculture in Nitra – Slovakia and Czech University of Life Science Prague, Czech Republic.

Hybrid tools for improved removal and surface finish of metals and non-metals

Increasing productivity and surface quality in manufacturing is inevitable for the efficient use of resources. Precision smooth surfaces are generally achieved through sequential milling, grinding, fine-grinding, and polishing. This research aims to increase productivity through a shorter process chain. Hybrid tooling technology is presented where an elastic fine grinding tool is integrated with a conventional milling cutter or a grinding tool, in order to produce smooth surfaces on metal and non-metals. A hybrid milling cutter with an elastic tool could produce a low-waviness surface as compared to that of a milled-only surface. Surface roughness could be reduced by almost 50% using the hybrid milling tool on a steel workpiece. A monocrystalline silicon surface was machined with a hybrid grinding tool. Surface roughness of Ra ~ 100 nm could be obtained for hybrid tool in comparison to Ra ~ 200 nm with grinding only tool. The surface machined with a hybrid tool showed ductile mode cutting streaks, whereas, the ground surface was observed to be completely in brittle mode. Additionally, the hybrid tools also showed reduced machining vibrations due to the damping effect induced by the elastic tool member.

Construction of functional grain boundary clusters for casting large-size and high-quality monocrystalline silicon ingots

The construction of functional grain boundary clusters (FGBCs) effectively prevents the overgrowth of mc-Si at the edge of cast mono-Si ingots. This approach significantly increases the mono-Si proportion and greatly enhances the defect distribution.