Semiconductor Manufacturing Industry Research Articles

Enhancing manual inspection in semiconductor manufacturing with integrated augmented reality solutions

On-site routine inspection often remains a manual operation in the semiconductor manufacturing industry because implementing automated solutions can be costly and technically challenging in such a highly controlled and complex environment. The manual inspection is prone to errors due to the impact of demanding physical and mental workloads. This paper presents an integrated Augmented Reality (AR) solution developed to assist manual inspection tasks in the supporting areas of semiconductor manufacturing, referred to as the sub-fab. The solution is accessible to a human worker wearing an AR headset during the inspection process at the location. We propose a system framework to deploy computational intelligences of varying granularity provided by the solution across cloud, edge, and device levels, accommodating constraints within the sub-fab. A machine maintenance module helps estimate and monitor the health condition of running scrubbers. Incorrect intentions performed by the worker on the scrubber control panel are detected through hand gesture recognition. This instantly prompts warning messages in the AR headset to prevent subsequent wrong actions. The solution can also identify abnormal device states through 6D pose estimation of objects enabled by machine learning models. A test scenario demonstrates how these functional features enhance the inspection efficiency and quality by reducing human workloads. This work demonstrates that semiconductor manufacturing may require AR-assisted functions different from those needed or common in other industrial sectors. It also highlights the potential of AR technology for reducing operational human errors in manual tasks.

Dual-ion permeation Janus membrane-assisted element reconstitution system enables fluorosilicate-oriented recovery from fluoride-rich and silica-rich wastewaters

Rapid development of semiconductor manufacturing and photovoltaic industry leads to significant generation of fluoride-rich and silica-rich wastewaters. Due to the emphasis on circular economy and resource recovery, there is a shift from regarding wastewater as waste to a recoverable resource. In this study, we present a uniquely designed dual-ion permeation Janus membrane (DPM)-assisted element reconstitution system (MERS) for selective recovery of high-value fluorosilicates from fluoride-rich and silica-rich wastewaters. The MERS with a configuration of cation-exchange membrane/bipolar membrane/DPM/anion-exchange membrane/cation-exchange membrane achieved HF formation in silica chamber and further SiF62- generation from the reaction of HF with SiO2. Driven by the electric field, SiF62- was then transported through DPM into acid chamber for fluorosilicates selective recovery. The DPM with positively-charged nanoporous substrate/negatively charged active layer enhanced electrostatic interaction for SiF62-/H+ transport and steric exclusion for coexisting foulants rejection. Ion transport mechanism analysis demonstrated DPM enhanced SiF62- migration while inhibiting back diffusion by electrostatic interaction and steric exclusion. Through the application of DPM, MERS showed rejections over 99% for nanoparticles and over 90% for organics. Thus, MERS stably selectively recovered SiF62- with recovery rate over 85% and fluorosilicates purity over 99.5%. Compared to traditional technologies, MERS achieved valuable resource recovery with the advantages of simple operation, small footprint and no secondary pollutant generation. Overall, this study provides a new strategy for simultaneous recovery of fluoride and silica from different waste streams, enabling a more sustainable strategy for semiconductor and photovoltaic industries development.

Modification of New Semiconductor Materials by Doping

In the era of information, people’s lives are inseparable from electronic equipments. Semiconductors are key materials for manufacturing electronic equipment, which can be applied in multiple fields such as integrated circuits, solar cells, and so on. However, pure semiconductors have a single property, which are difficult to meet the design and usage requirements of different electronic devices. To solve the problem, introducing heteroatoms for modification to make them have more suitable performance will be a good way. Good semiconductor performance generally relies on precise control of the doping process. Different types of semiconductor materials typically require specific doping techniques to improve its performance. Adding small amounts of heteroatoms to pure semiconductors can significantly improve their conductivity by forming p/n junctions or achieving specific functions. Doping technology has been widely used in the semiconductor manufacturing industry. It is of great significance for the manufacturing of chips, photovoltaics and other devices. At present, there are still many shortcomings in doping technology. It may come across the semiconductor structure failure caused by doping, or difficulty in meeting performance requirements for use. Thereofore, continuous research and improvement are still needed. This paper reviewed the applications and the doping techniques of various semiconductor materials including two-dimensional semiconductors and organic semiconductors. In addition, it also provides prospects for their related applications and development.

Corrosion-life prediction model for 316L stainless steel under electronic special gases containing trace moisture employed in semiconductor manufacturing industry

Special gases with trace moisture cause the formation of dynamic acidic microdroplets (DMD), which results in corrosion of semiconductor manufacturing devices. In this study, a predictive model for corrosion damage in 316L stainless steel (SS) was developed by combining the DMD process and the pitting initiation model. The DMD process was modeled using the BET model to describe the moisture-to-solution conversion. The pitting initiation model was reconstructed by incorporating the Sridhar model, temporal corrosion model, and Macdonald model. Finally, the predicted results were validated by various experimental data, indicating that the prediction model was accurate and highly reliable.

Trustworthy AI for semiconductor manufacturing

Trustworthy AI for semiconductor manufacturing Marcel van Gerven, a Professor of Artificial Cognitive Systems at Radboud University, discusses how his team’s research on AI models, algorithms, and demonstrators could serve as a blueprint for the semiconductor manufacturing industry. Artificial Intelligence (AI) is a key enabling technology that, when used responsibly, can bring about significant positive societal impact. One of the initiatives to foster this positive impact is the Dutch ROBUST ‘Trustworthy AI-based Systems for Sustainable Growth’ program. This ten-year Long Term Program (LTP) is a human- centered research program in AI that brings together knowledge institutes, industry, governmental organizations, and societal partners to develop AI-based methods and tools designed to create social impact and promote sustainable growth (Figure 1).

Entangling gates on degenerate spin qubits dressed by a global field

Semiconductor spin qubits represent a promising platform for future large-scale quantum computers owing to their excellent qubit performance, as well as the ability to leverage the mature semiconductor manufacturing industry for scaling up. Individual qubit control, however, commonly relies on spectral selectivity, where individual microwave signals of distinct frequencies are used to address each qubit. As quantum processors scale up, this approach will suffer from frequency crowding, control signal interference and unfeasible bandwidth requirements. Here, we propose a strategy based on arrays of degenerate spins coherently dressed by a global control field and individually addressed by local electrodes. We demonstrate simultaneous on-resonance driving of two degenerate qubits using a global field while retaining addressability for qubits with equal Larmor frequencies. Furthermore, we implement SWAP oscillations during on-resonance driving, constituting the demonstration of driven two-qubit gates. Significantly, our findings highlight how dressing can overcome the fragility of entangling gates between superposition states and increase their noise robustness. These results constitute a paradigm shift in qubit control in order to overcome frequency crowding in large-scale quantum computing.

Feature Selection for Data Classification in the Semiconductor Industry by a Hybrid of Simplified Swarm Optimization

In the semiconductor manufacturing industry, achieving high yields constitutes one of the pivotal factors for sustaining market competitiveness. When confronting the substantial volume of high-dimensional, non-linear, and imbalanced data generated during semiconductor manufacturing processes, it becomes imperative to transcend traditional approaches and incorporate machine learning methodologies. By employing non-linear classification models, one can achieve more real-time anomaly detection, subsequently facilitating a deeper analysis of the fundamental causes behind anomalies. Given the considerable dimensionality of production line data in semiconductor manufacturing, there arises a necessity for dimensionality reduction to mitigate noise and reduce computational costs within the data. Feature selection stands out as one of the primary methodologies for achieving data dimensionality reduction. Utilizing wrapper-based heuristics algorithms, although characterized by high time complexity, often yields favorable performance in specific cases. If further combined into hybrid methodologies, they can concurrently satisfy data quality and computational cost considerations. Accordingly, this study proposes a two-stage feature selection model. Initially, redundant features are eliminated using mutual information to reduce the feature space. Subsequently, a Simplified Swarm Optimization algorithm is employed to design a unique fitness function aimed at selecting the optimal feature subset from candidate features. Finally, support vector machines are utilized as the classification model for validation purposes. For practical cases, it is evident that the feature selection method proposed in this study achieves superior classification accuracy with fewer features in the context of wafer anomaly classification problems. Furthermore, its performance on public datasets further substantiates the effectiveness and generalization capability of the proposed approach.

Research on the minimum detection limit for measuring high-purity gases by gas chromatography

Abstract At present, the application fields of high-purity gases (purity reached 99.999% and above) include the semiconductor manufacturing industry, chemical analysis, fuel cells, aerospace, medicine, etc., and the quality and purity requirements of high-purity gases are also getting higher and higher. As the only detector that can detect ppb-level trace impurities, the pulsed discharge helium ionization detector (PDHID) is the most used detector in the field of high-purity gases. The minimum detection limit of existing PDHID-related analysis methods is usually calculated theoretically. In order to verify the actual minimum detection limit of PDHID, a multi-group gas distribution system was used in this paper to dilute the permanent gas components in the high-purity gas, and the gas chromatograph with pulsed discharge helium ionization detector (GC-PDHID) was used to detect the obtained different concentrations of standard gases. The experimental results showed that PDHID possessed high sensitivity, good linearity, and a wide linear range. The actual minimum detection limit for the permanent gas components in the high-purity gas could reach the ng/g (ppb) level, which could realize the detection of ultra-low concentration samples, and further verify the actual minimum detection limit of PDHID. This experiment provides a reference basis for the analysis of trace gases in the gas industry, especially in industries such as high-purity and ultra-high-purity gases and gas for the electronics industry, and is of great significance in actual production work.

A Double Deep Q-Network framework for a flexible job shop scheduling problem with dynamic job arrivals and urgent job insertions

In the semiconductor manufacturing industry, the Dynamic Flexible Job Shop Scheduling Problem is regarded as one of the most complex and significant scheduling problems. Existing studies consider the dynamic arrival of jobs, however, the insertion of urgent jobs such as testing chips poses a challenge to the production model, and there is an urgent need for new scheduling methods to improve the dynamic response and self-adjustment of the shop floor. In this work, deep reinforcement learning is utilized to address the dynamic flexible job shop scheduling problem and facilitate near-real-time shop floor decision-making. We extracted eight state features, including machine utilization, operation completion rate, etc., to reflect real-time shop floor production data. After examining machine availability time, the machine's earliest available time is redefined and incorporated into the design of compound scheduling rules. Eight compound scheduling rules have been developed for job selection and machine allocation. By using the state features as inputs to the Double Deep Q-Network, it is possible to acquire the state action values (Q-values) of each compound scheduling rule, and the intelligent agent can learn a reasonable optimization strategy through training. Simulation studies show that the proposed Double Deep Q-Network algorithm outperforms other heuristics and well-known scheduling rules by generating excellent solutions quickly. In most scenarios, the Double Deep Q-Network algorithm outperforms the Deep Q-Network, Q-Learning, and State-Action-Reward-State-Action (SARSA) frameworks. Moreover, the intelligent agent has good generalization ability in terms of optimization for similar objectives.

Structural dependency of pyridine carboxylic acid isomers in metal-organic coordination adsorption for copper corrosion inhibition

We systematically investigate the adsorption behavior and corrosion inhibition efficacy of pyridine carboxylic acid isomers (Picolinic, Nicotinic, Isonicotinic acids) on Cu films in alkaline conditions. By integrating theoretical analyses, density functional theory (DFT) calculations, and experimental techniques, we demonstrate the underlying mechanisms governing the adsorption affinities and corrosion inhibition properties of these isomers. Our results show that Nicotinic acid exhibits the highest adsorption affinity and corrosion inhibition efficiency on Cu films, attributed to its favorable electronic and steric properties that enhance nucleophilic interaction and electron donation capabilities. On the other hand, Picolinic acid hindered by steric effects shows the lowest adsorption affinity and inhibition efficiency. The electrochemical studies confirm that Nicotinic acid significantly lowers the corrosion current density and enhances the polarization resistance, offering superior protection against corrosion. Furthermore, the chemical and electrochemical interactions between the inhibitors and Cu films are detailed, with insights into the modification of the Cu surface’s electric structure and the formation of protective layers against oxidation. Our research contributes important insights for the development and use of effective corrosion inhibitors for Cu, especially in semiconductor manufacturing and other industries, highlighting the importance of an isomer’s molecular structure in its ability to adsorb and prevent corrosion.

Memory type Bayesian adaptive max-EWMA control chart for weibull processes

The simultaneous monitoring of both the process mean and dispersion has gained considerable attention in statistical process control, especially when the process follows the normal distribution. This paper introduces a novel Bayesian adaptive maximum exponentially weighted moving average (Max-EWMA) control chart, designed to jointly monitor the mean and dispersion of a non-normal process. This is achieved through the utilization of the inverse response function, particularly suitable for processes conforming to a Weibull distribution. To assess the effectiveness of the proposed control chart, we employed the average run length (ARL) and the standard deviation of run length (SDRL). Subsequently, we compared the performance of our proposed control chart with that of an existing Max-EWMA control chart. Our findings suggest that the proposed control chart demonstrates a higher level of sensitivity in detecting out-of-control signals. Finally, to illustrate the effectiveness of our Bayesian Max-EWMA control chart under various Loss Functions (LFs) for a Weibull process, we present a practical case study focusing on the hard-bake process in the semiconductor manufacturing industry. This case study highlights the adaptability of the chart to different scenarios. Our results provide compelling evidence of the exceptional performance of the suggested control chart in rapidly detecting out-of-control signals during the hard-bake process, thereby significantly contributing to the improvement of process monitoring and quality control.

Ultrasonic non-destructive evaluation of w-GaSe at different temperatures

ABSTRACT The present work includes non-destructive evaluation of mechanical, thermal and ultrasonic properties of inorganic semiconducting wurtzite gallium selenide (w-GaSe) material using Lenard Jones’s potential approach at different temperatures. For elastic and mechanical properties, second-order elastic constants (SOECs), elastic moduli, anisotropy factors are estimated within the temperature range of 300-1500K. The ultrasonic and thermo-physical properties are calculated within the same temperature range utilising estimated SOECs, density and lattice parameters of the chosen material. The elastic, mechanical, and ultrasonic properties of the chosen semiconducting material have been found to decrease with an increase in temperature. The obtained results have been compared and analysed for justification and to explore the inherent properties of GaSe for semiconductor manufacturing industries.

Corrosion of stainless steels and corrosion protection strategies in the semiconductor manufacturing industry: a review

Abstract Stainless steels are used extensively in semiconductor manufacturing as chamber, structure component and gas delivery systems. The corrosion in the aggressive gas in the semiconductor manufacturing industry leads to particle release, contaminating wafers and limiting their application. Moisture content can accelerate the corrosion rate of stainless steel. In a high-temperature environment, the corrosion is determined by the synergistic effect of the vapour of the corrosion product and thermal ageing. To eliminate corrosion, lots of efforts have been performed and categorized into three aspects: (1) Material purification using innovating metallurgy techniques, especially vacuum induction melting (VIM) and vacuum arc remelting (VAR). The ultra-pure stainless steel minimizes the inclusion in stainless steel, suppressing the breakdown of the passive film. (2) Smoothing the surface by polishing; the polishing surface shows hydrophobic behaviour and decreases moisture absorption. (3) Applying surface and coating techniques against corrosion, including passivation treatment and electroplating/electroless Ni-based coating. Herein, the techniques mentioned above are reviewed, and the prospect and development of stainless steel in the semiconductor manufacturing industry are forecasted.

Energy efficient dry-storage systems in the semiconductor manufacturing industry

The semiconductor industry is a rapidly-changing one and many new electronic devices are being developed every year. As components get smaller and thinner, humidity-related problems increase since moisture can easily penetrate to critical areas. There are various techniques to prevent moisture ingress during manufacturing and assembly. One of the best methods is to store semiconductor devices in dry storage enclosures (also known as Dry Boxes) between consequent processes and operations. However, since they are typically supplied with Compressed Dry Air (CDA), Dry Boxes tend to be energy intensive. By using an industrial setup at a manufacturing facility in Malta as a case study, an empirical assessment was carried out to investigate the use of Dry Boxes from an energy consumption perspective. The energy performance of Dry Boxes using CDA was compared to that of Dry Boxes using a desiccant-rotor technology. The relative humidity and air temperature inside the Dry Box were monitored in order to assess the behaviour of both technologies with time. An analysis of the Dry Box conditions was conducted to better understand the sources of any existing losses and to identify whether scope for improvement, if any, existed. The study showed that even if the drying technology on the supply side is not changed, improvements on the demand side can have an effective reduction in resource consumption. Using the desiccant rotor instead of CDA to dehumidify the Dry Box did not decrease the energy consumption. However, it was positively concluded that by reducing the infiltration rate and the unoccupied internal volume of the Dry Box, the CDA consumption and hence the energy and carbon footprint of the process decreased by 26%, contributing towards the sustainability goals of the manufacturing industry.

Revolutionizing digital marketing: unveiling the impact of influencer marketing, AI-driven customer support, and voice search optimization on engagement and efficiency on the example of the semiconductor manufacturing industry

In the rapidly evolving landscape of semiconductor manufacturing, this research paper explores the application of pivotal digital marketing trends and strategic innovations to reshape contemporary business strategies within the industry. Recognizing the significance of staying at the forefront of these transformations, our study aims to provide a nuanced understanding of three prominent digital marketing innovations: Influencer Marketing, Artificial Intelligence (AI)-driven Customer Support, and Voice Search Optimization. Through a rigorous methodology integrating literature review and case study analysis specific to semiconductor manufacturing in India, we unveil the successful implementation and tangible impact of these innovations on user engagement, brand perception, and overall marketing efficacy within the semiconductor sector of India. The research was done in 2022-2023 over the semiconductor companies working in India (HCL Technologies, Bharat Electronics Limited, ABB India Limited, Vedanta, CG Power and Industrial Solutions Limited, and Havells India Limited). Comparative analyses against traditional methods reveal significant improvements tailored to this industry: Influencer Marketing campaigns demonstrated a 25% increase in reach and engagement compared to conventional advertising. The integration of AI-driven Customer Support resulted in a 30% reduction in response time, enhancing customer satisfaction scores by 15%. Furthermore, Voice Search Optimization strategies yielded a 20% improvement in search accuracy, outperforming traditional text-based Search Engine Optimization (SEO) approaches within the semiconductor manufacturing context. Delving into challenges and opportunities associated with each innovation within the semiconductor industry, we provide evidence-based recommendations for businesses seeking to navigate the dynamic digital marketing landscape in this specialized domain. As semiconductor manufacturing continues to redefine its business ecosystem, embracing these innovations emerges as a cornerstone for sustained success in the digital era within this unique sector.

Application of 1D ResNet for Multivariate Fault Detection on Semiconductor Manufacturing Equipment.

Amid the ongoing emphasis on reducing manufacturing costs and enhancing productivity, one of the crucial objectives when manufacturing is to maintain process tools in optimal operating conditions. With advancements in sensing technologies, large amounts of data are collected during manufacturing processes, and the challenge today is to utilize these massive data efficiently. Some of these data are used for fault detection and classification (FDC) to evaluate the general condition of production machinery. The distinctive characteristics of semiconductor manufacturing, such as interdependent parameters, fluctuating behaviors over time, and frequently changing operating conditions, pose a major challenge in identifying defective wafers during the manufacturing process. To address this challenge, a multivariate fault detection method based on a 1D ResNet algorithm is introduced in this study. The aim is to identify anomalous wafers by analyzing the raw time-series data collected from multiple sensors throughout the semiconductor manufacturing process. To achieve this objective, a set of features is chosen from specified tools in the process chain to characterize the status of the wafers. Tests on the available data confirm that the gradient vanishing problem faced by very deep networks starts to occur with the plain 1D Convolutional Neural Network (CNN)-based method when the size of the network is deeper than 11 layers. To address this, a 1D Residual Network (ResNet)-based method is used. The experimental results show that the proposed method works more effectively and accurately compared to techniques using a plain 1D CNN and can thus be used for detecting abnormal wafers in the semiconductor manufacturing industry.

Artificial-Neural-Network-Driven Innovations in Time-Varying Process Diagnosis of Low-K Oxide Deposition.

To address the challenges in real-time process diagnosis within the semiconductor manufacturing industry, this paper presents a novel machine learning approach for analyzing the time-varying 10th harmonics during the deposition of low-k oxide (SiOF) on a 600 Å undoped silicate glass thin liner using a high-density plasma chemical vapor deposition system. The 10th harmonics, which are high-frequency components 10 times the fundamental frequency, are generated in the plasma sheath because of their nonlinear nature. An artificial neural network with a three-hidden-layer architecture was applied and optimized using k-fold cross-validation to analyze the harmonics generated in the plasma sheath during the deposition process. The model exhibited a binary cross-entropy loss of 0.1277 and achieved an accuracy of 0.9461. This approach enables the accurate prediction of process performance, resulting in significant cost reduction and enhancement of semiconductor manufacturing processes. This model has the potential to improve defect control and yield, thereby benefiting the semiconductor industry. Despite the limitations imposed by the limited dataset, the model demonstrated promising results, and further performance improvements are anticipated with the inclusion of additional data in future studies.

How Industrial Internet Platforms guide high-quality information sharing for semiconductor manufacturing? An evolutionary game model

To keep up with the times, the semiconductor manufacturing industry needs to break down barriers to data transmission. Since the manufacturing process of semiconductor materials is expensive, the material suppliers need to obtain a large amount of measurement information to optimize it. Therefore, its data cooperation with semiconductor manufacturers is particularly important. This paper uses Athnia, a data sharing platform for semiconductor manufacturing, as an example to illustrate the positive impact of high-quality information on the industry. Based on a three-party evolutionary game model of Industrial Internet Platform, semiconductor manufacturer and material supplier, this paper analyzes the factors affecting the choice of tripartite information sharing strategies. The results confirm the important role played by the Industrial Internet Platform in information sharing and highlight how the platform should adjust its strategies in different situations. This paper also identifies a number of external factors that affect the overall quality of information sharing. These findings will facilitate data sharing in semiconductor manufacturing.

Experimental and numerical study of Si annealed by laser sources of the visible spectrum (red and blue wavelength)

Laser annealing (LA) is an increasingly important process in the semiconductor manufacturing industry. So far, lasers of nonvisible spectrum have been mostly used for semiconductor applications. The study using a visible wavelength laser is not nearly found though it could be a potential tool to expand applications of the LA. In this study, the silicon (Si) deposited on oxide was annealed using laser sources of visible spectrum. LA of Si was performed using a single laser source of either red or blue wavelengths or a dual-laser source using both wavelengths. The surface morphology and microstructure of the annealed Si were analyzed. A numerical simulation was also conducted to predict the temperature and melt pool characteristics of Si during the annealing process. Surface roughness increased with increasing heat input (or power density) of the laser beam, owing to the considerable melting of Si. The dual laser using a relatively high power density produced Si with high polycrystallinity and large grains. However, the Si lattice constant difference induced by different annealing conditions was negligible. Simulation results showed that temperature, melt pool length, and duration increased with the power density of the dual-laser beam. This contributed to the growth of large Si grains.

Enterprise Architecture for Equipment Performance Analysis Based on Internet of Things (IoT) Technology in the Semiconductor Manufacturing Industry

This study proposes a concept for establishing an Enterprise Architecture in the semiconductor manufacturing industry for equipment performance analysis using Internet of Things (IoT) technology. The plan is to implement The Open Group Framework approach as Enterprise Architecture in the manufacturing analytics department. The TOGAF approach improves data governance in the business process and analytics department. Manufacturing focuses on providing excellent and high-quality products to customers. As a result, the manufacturer must use a data analytics application to monitor the performance of the equipment. The performance of the equipment is monitored around the clock to ensure that it meets the requirements and does not exceed the threshold. The business process, data from the warehouse, and how it is processed will be discussed. Implementing Enterprise Architecture in manufacturing will also be discussed, focusing on the three layers of the TOGAF Architecture Development Method (ADM). The three layers are business, technology, and application. The Enterprise Architecture framework is a blueprint for the architecture used to align the business and information technology. Enterprise architecture optimises business processes and structures processes and functions to integrate information technology into the business. The proposed Enterprise Architecture for equipment performance analysis in the semiconductor manufacturing industry can be used as a guideline for implementing a comprehensive framework for tool performance monitoring.