Identification of Defective Two Dimensional Semiconductors by Multifractal Analysis: The Single-layer MoS2{\rm MoS_2} Case Study

Two dimensional semiconductor such as single-layer transition metal dichalcogenides (SL-TMD) have attracted most attentions as an atomically thin layer semiconductor materials. Typically, lattice point defects (sulfur vacancy) created by physical/chemical method during growth stages, have disadvantages on electronic properties. However, photoluminescence (PL) spectroscopy is conventionally used to characterize single-layer films but until now it has not been used to show the presence of defects or estimate their population due to overall similarity of general feature PL spectra. To find a feasible and robust method to determine the presence of point defects on single layer ${\rm MoS_2}$ without changing the experimental setup, Multifractal Detrended Fluctuation Analysis (MF-DFA) and Multifractal Detrended Moving Average Analysis (MF-DMA) are applied on the PL spectrum of single layer ${\rm MoS_2}$. We compare the scaling behavior of PL spectrum of pristine and defective single layer ${\rm MoS_2}$ determined by MF-DFA and MF-DMA. Our results reveal that PL spectrum has multifractal nature and different various population of point defects (sulfur vacancy) on single layer ${\rm MoS_2}$ change dramatically multifractality characteristics (Hurst, H\"older exponents) of photoluminescence spectrum. It is exhibited creating more lattice point leads to smaller fluctuations in luminescent light that it can help to design special defect structure for light emitted devices. The relative populations of point defects are almost elucidated without utilizing expensive characterization instruments such as scanning tunneling microscopy (STM) and high resolution transmission electron microscopy (HR-TEM).Comment: 30 pages, 9 figures and 1 tabl

PSS design through Design for Supply Chain: State of the art review.

Design for X (DfX) approaches are very important for designing products with a focus on whole lifecycle, to achieve cost reduction and product quality. The move to achieve competitiveness and unique offerings have resulted in the switch from a product to a Product Service Systems (PSS) business model. DfX concept is insufficient to address the complexity of PSS, therefore, additional concepts such as Design for Product Service Supportability (DfPSSu) are emerging. Existing research argued the role of support in ensuring customer satisfaction, revenue generation etc., which strengthens the motivation for PSS and servitization. The integration of support services into PSS has initiated the focus on DfPSSu, aiming at the synergic use of the different DfX approaches to concurrently support the services with the product features according their own heterogeneity. PSS complexity necessitates collaboration within the Supply Chain (SC) to deliver value to the customer, yet existing research focuses on individual firms. This highlights the importance of value creation in Design for SC (DfSC) in order to achieve competitiveness. This research would explore DfX from a value creation perspective while investigating the place of DfSC into the DfPSSu concept. This because DfSC encourages innovation in linking product design, process design and SC design together, according to the Concurrent Engineering paradigm. While there is need to DfSC, this idea is under-researched in literature. This paper would share the findings from a state of the art review of DfSC in relation to DfPSSu, identifying the evolution of the concept while identifying much research gap in understanding and application of this concept in theoretical and empirical research

A multi-phase analytics framework for supply chain supplier selection and order allocation with delay risks and Industry 4.0 readiness

Numerous studies have addressed the Supplier Selection and Order Allocation (SSOA) problem, focusing on optimal quantity allocation. However, in practice, suppliers often fail to deliver allocated quantities on time due to operational delays or disruptions. Thus, incorporating supplier delays into order allocation decisions is essential. This paper introduces a multi-phase optimization framework that integrates the impact of delays into the SSOA process. In the initial phase, several Machine Learning (ML) algorithms are employed to predict delay probabilities at the order level. This study is the first to utilize ML-based delay probability predictions - rather than binary classification (on-time vs. delayed) - to determine optimal supplier allocations. The algorithms are evaluated using performance metrics such as accuracy, F1 score, precision, recall, and AUC, with TOPSIS used to select the most effective algorithm. Predicted probabilities are then aggregated to the supplier level for integration into the optimization model. Given the growing importance of Industry 4.0, the framework incorporates an Industry 4.0 Readiness Index (IRI), constructed using linguistic terms and interval numbers to handle subjective evaluations. The SWARA method is used to assign weights to evaluation criteria. These elements are embedded in a bi-objective optimization model, solved via the augmented ε-constraint method, aiming to minimize supply chain costs while maximizing suppliers' IRI scores. A numerical example based on a real-world case study validates the approach. Results show significant changes in supplier allocations when delay probabilities are considered, with a 4.84 % increase in total supply chain cost, primarily due to increased procurement in certain periods

Optimisation multi-objectif de la variété des produits dans un processus de personnalisation de masse : approche pour la conception concourante

The doctoral research focuses on developing a method to evaluate level of product varieties that can be offered to customers in mass customization environment. For this purpose, we use three-dimensional concurrent engineering (3-DCE) approach that considers different decision points from domains of process and supply chain when developing a new product. To achieve our main aim of thesis, that is to investigate level of product varieties which are proper to offer customers, we integrate several decision points including selection of features or component options, selection of a manufacturing method, positioning of customer order decoupling point (CODP) as one of main decision point in MC and selection of suppliers. We propose two multi-objective optimization models based on values perceived by customers and company and by developing qualitative and quantitative performance indicators. Evaluation of qualitative performance indicators is done by linguistic terms and fuzzy numbers. In order to produce the results closer to real situations, we, also, apply interval data for some parameters and use from a modified NSGAII for interval data to solve the models. Also, for helping to make decision, we propose a new ranking method based on interval data. We test and validate our models with a case study and in final, represent some conclusions and future directions in order to improve the proposed method.Notre thèse de doctorat se concentre sur le développement d'une méthode pour évaluer le niveau de variétés de produits qui peut être offert aux clients dans l'environnement de la personnalisation de masse. A cet effet, nous utilisons l'approche d’ingénierie concourante 3D engineering (3-DCE) qui considère différents points de décision : processus, chaine logistique et développement des produits. Afin d'enquêter sur le niveau de variétés de produits qui adapté à nos clients, nous intègrons des principaux points de décision y compris la sélection des fonctionnalités ou options de composants, sélection d'un procédé de fabrication, positionnement point de découplage des commandes (CODP) et sélection des fournisseurs. Nous proposons deux modèles d'optimisation multi-objectifs fondés sur les valeurs perçues par les clients et l'entreprise en développant des indicateurs de performance qualitatifs et quantitatifs. L’évaluation des indicateurs de performance qualitatifs est effectuée par les termes linguistiques et nombres flous. Pour rendre les résultats plus proches de situations réelles, nous appliquons aussi des données d'intervalle pour certains paramètres et utilisons NSGAII modifié pour résoudre les modèles. Nous testons et validons nos modèles avec une étude de cas et nous présentons enfin quelques conclusions et les orientations futures en vue d'améliorer la méthode proposée

LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns

Abstract In this paper, we present a machine learning-based approach that leverages Long Short-Term Memory (LSTM) networks combined with a sliding window technique for feature extraction, aimed at accurately predicting point defect percentages in semiconductor materials based on simulated X-ray Diffraction (XRD) data. The model was initially trained on silicon-simulated XRD data with defect percentages ranging from 1 to 5%, enabling it to predict defect percentages from 0 to 10% in silicon and other semiconductor materials, including AlAs, CdS, GaAs, Ge, and ZnS. Through extensive experimentation, we explored different sequence lengths and LSTM units, identifying the optimal configuration as a sequence length of 3501 and 4500 units, which yielded the best results. The model’s mean absolute error at 4500 units was 0.021, the lowest among the LSTM configurations tested. The sliding window technique plays a crucial role in capturing temporal dependencies within the XRD data, allowing the model to generalize to other semiconductor materials. Additionally, we observed that increasing defect percentages consistently led to a rise in background intensity. We further examined the relationship between crystal structure and defect precentage predictions, uncovering consistent trends for materials with Diamond Cubic and Zinc Blende structures. This LSTM-based method offers a novel approach to predicting defect percentages using simulated XRD patterns of materials