Semiconductor Industry Research Articles

A reinforcement learning-based approach for solving multi-agent job shop scheduling problem

Wafer processing is the most expensive, time-consuming, and complex stage in semiconductor manufacturing. It varies significantly based on orders of customers (agents). Optimising the wafer processing flow in a multi-agent scenario can meet customised requirements, speed up delivery, and reduce costs. This work models wafer processing as a multi-agent job shop scheduling problem (MAJSP) with release dates. The objective is to minimise the total weighted makespan of agents. To address both dynamic and static scheduling scenarios in the MAJSP context, two deep reinforcement learning-based (DRL) methods are proposed. In a dynamic scheduling scenario, the statuses of orders and production resources can change at any moment. A DRL method called Graph Transformer Network (GTN) is proposed to rapidly generate high-quality solutions. In a static scheduling scenario, the production plan can be formulated based on predetermined demand and resource conditions. A novel hybrid method (GTN-DABC) that combines GTN with the discrete artificial bee colony algorithm (DABC) is proposed to provide high-quality production plans for manufacturers within an acceptable computation time. Experimental results demonstrate that the proposed GTN outperforms existing heuristics, and the well-designed GTN-DABC is more competitive than other meta-heuristics.

Bandwidth-aware fast inverse lithography technology using Nesterov accelerated gradient

Inverse lithography technology (ILT) is a leading-edge method for improving the resolution and image fidelity of optical lithography systems in semiconductor manufacturing. However, the massive computational complexity of ILT presents the inherent runtime bottleneck, which hinders its application to the large-scale lithography layouts. This paper innovates a fast ILT method by reducing both of the inherent algorithmic complexity and the required iteration number. Based on the diffraction-limited nature of optical lithography system, a bandwidth-aware acceleration approach is applied to reduce the calculation complexities of the forward imaging model and inverse gradient computation in each ILT iteration. In addition, a carefully designed algorithm based on the Nesterov accelerated gradient (NAG) is developed to achieve nearly quadratic convergence rate, thereby reducing the total number of iterations required to obtain the optimal solution. Numerical experiments show that the proposed method can improve the speed by hundreds of times over the traditional gradient-based ILT algorithms without sacrificing accuracy.

A New Back-End-Of-Line Ferroelectric Field-Effect Transistor Platform via Laser Processing.

The discovery of ferroelectricity in hafnia-based materials has revitalized interest in realizing ferroelectric field-effect transistors (FeFETs) due to its compatibility with modern microelectronics. Furthermore, low-temperature processing by atomic layer deposition offers promise for realizing monolithic three-dimensional (M3D) integration toward energy- and area-efficient computing paradigms. However, integrating ferroelectrics with channel materials in FeFETs for M3D integration remains challenging due to the dual requirement of a high-quality ferroelectric-channel interface and low-power operation, all while maintaining back-end-of-line (BEOL)-compatible fabrication temperatures. Recent studies on 2D semiconductors and metal oxide channels highlight these challenges. Polycrystalline silicon (poly-Si), a channel material long integrated into the semiconductor industry, presents a promising alternative; however, its high fabrication temperature has hindered its applications to M3D integration. To overcome this challenge, wedemonstrates a BEOL-compatible FeFET platform using poly-Si channels fabricated via locally-confined laser thermal processing and hafnia-based ferroelectrics by low-temperature atomic layer deposition with wafer-scale uniformity. The local nature of the laser processing mitigates the trade-off between the high-temperature crystallization for the quality of the interface and BEOL thermal budget constraints. The laser-processed FeFETs boast the largest effective memory widow for all BEOL-compatible FeFETs. Moreover, the fabricated FeFETs are integrated into wafer-scale synaptic arrays for neuromorphic computing, achieving record-high energy efficiency. Therefore, this work establishes a promising BEOL-compatible FeFET materials platform toward M3D integration.

Post Tungsten CMP Cleaning: Optimization for Cleaning Efficiency and Corrosion Reduction

Abstract Tungsten chemical-mechanical planarization (W CMP) is among the first applications of the CMP process implemented in high volume semiconductor manufacturing. Alkaline chemicals such as diluted NH4OH have been the predominant choice of post W CMP clean due to charge repulsion between W and dielectric/abrasive surface in high pH. However, W dissolution occurs spontaneously above pH ~4.0. Consequently, W corrosion can happen during post-CMP cleaning. From that perspective, acidic chemistry (e.g., pH< 4.0) seems a more benign choice to protect W, although at such low pH, isoelectric potential curves suggest coulombic attraction between W and oxide/silica surfaces, making it unfavorable for particle removal. The above pH dilemma for post-CMP cleaning and W protection can be managed by adding corrosion inhibitor to an alkaline chemical. Results from blanket and patterned wafers suggest reduction in surface defects and W dissolution can be accomplished simultaneously by an alkaline clean chemical with corrosion inhibitor (pH ~ 9.78), which shows high cleaning efficiency and reduced W loss than citric acid-based chemical (pH ~ 2.66), DIW, and diluted NH4OH. In addition, we show that DIW alone can induce severe W dissolution in features of fine geometry. Possible counter measures against such DIW-induced W corrosion are discussed.

Plasma Treatment Technologies for GaN Electronics

Nowadays, the third-generation semiconductor led by GaN has brought great changes to the semiconductor industry. Utilizing its characteristics of a wide bandgap, high breakdown Electric field, and high electron mobility, GaN material is widely applied in areas such as 5G communication and electric vehicles to improve energy conservation and reduce emissions. However, with the progress in the development of GaN electronics, surface and interface defects have become a main problem that limits the further promotion of their performance and stability, increasing leakage current and causing degradation in breakdown voltage. Thus, to reduce the damage, Plasma treatment technologies are introduced in the fabrication process of GaN electronics. Up to now, designs like the high-resistivity p-GaN cap Layer, passivating termination, and surface recovery process have been established via Plasma treatment, reaching the goals of normally-off transistors, diodes with high breakdown voltage and high-reliability GaN electronics, etc. In this article, hydrogen, fluorine, oxygen, and nitrogen Plasma treatment technologies will be discussed, and their application in GaN electronics will be reviewed and compared.

Dependence and information flow among U.S technology industries

ABSTRACT The technology sector serves as a fundamental pillar for all industry sectors in the economy, playing a central role in driving worldwide economic growth. It particularly distinguishes itself in the U.S., representing approximately one-third of the global technology market. However, the technology sector is not a homogenous group of similarly focused companies, its industries vary significantly in their characteristics. This paper analyzes the relationships among U.S. technology industries defined by the Global Industry Classification Standard using Vine copulas and transfer entropy. It spans 1 January 2010–31 January 2024, to clarify intra-sectoral heterogeneity and identify key industries predicting the performance of others. Our findings highlight the predictive power of the semiconductor industry, with significant information transmission to all other industries. Contrarily, the flow in the opposite direction is significant in only 3 of 5 cases. The identification of semiconductors’ central role is particularly valuable for both retail and institutional investors concentrating on technology stocks, as it may indicate future developments in the entire sector. Moreover, due to the industry’s global importance, shocks in the semiconductor industry could propagate across the entire technology sector, leading to broader economic instability. Therefore, the semiconductor industry should be closely monitored by the policymakers too.

Formal Verification with ABV : A Superior Alternative to UVM for Complex Computing Chips

This article explores the evolution and effectiveness of formal verification enhanced with Assertion-Based Verification (ABV) as a superior alternative to traditional Universal Verification Methodology (UVM) in complex computing chip design. Through analysis of implementation data from major semiconductor companies, including Intel's Core i7 and IBM's POWER processors, the article demonstrates how formal methods achieve up to 100% coverage of critical modules compared to UVM's typical 80-85% coverage. The research presents quantitative evidence of formal verification's advantages, including a 25-30% reduction in verification cycles, a 40% increase in security vulnerability detection, and a 55% reduction in overall verification time through advanced techniques such as compositional verification and proof decomposition. The article also addresses scalability challenges and resource optimization strategies, providing a comprehensive framework for implementing formal verification in modern chip design workflows.

Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN

The precise monitoring of chemical reactions in plasma-based processes is crucial for advanced semiconductor manufacturing. This study integrates three diagnostic techniques—Optical Emission Spectroscopy (OES), Quadrupole Mass Spectrometry (QMS), and Time-of-Flight Mass Spectrometry (ToF-MS)—into a reactive ion etcher (RIE) system to analyze CF4-based plasma. To synchronize and integrate data from these different domains, we developed a Tri-CycleGAN model that utilizes three interconnected CycleGANs for bi-directional data transformation between OES, QMS, and ToF-MS. This configuration enables accurate mapping of data across domains, effectively compensating for the blind spots of individual diagnostic techniques. The model incorporates self-attention mechanisms to address temporal misalignments and a direct loss function to preserve fine-grained features, further enhancing data accuracy. Experimental results show that the Tri-CycleGAN model achieves high consistency in reconstructing plasma measurement data under various conditions. The model’s ability to fuse multi-domain diagnostic data offers a robust solution for plasma monitoring, potentially improving precision, yield, and process control in semiconductor manufacturing. This work lays a foundation for future applications of machine learning-based diagnostic integration in complex plasma environments.

Post-Covid Impact on Supply Chain Risk Management: A Case Study of a Semiconductor Company

Post-Covid Impact on Supply Chain Risk Management: A Case Study of a Semiconductor Company

The risk spillover between geopolitical risk and China’s 5G, semiconductor and rare earth industries

Spillover effect refers to the phenomenon that when an economic entity, industry or market encounters a change or shock, its impact is transmitted to other entities, industries or markets. In recent years, 5G, semiconductors and rare earths have successively become geopolitical battlegrounds. In this paper, sparrow search algorithm was used to optimize the parameters of variational mode decomposition (VMD), then use the optimized VMD to decompose the geopolitical risk (GPR), 5G, semiconductor and rare earth time series data, and exploit fuzzy c-means clustering algorithm to obtain low-frequency and high-frequency time series. Finally we investigates the connectedness between GPR and China's 5G, semiconductor, and rare earth industries based on TVP-VAR model in different cycles, as well as between these industries. The findings reveal that there exists risk spillover between GPR and the three industries, which is significantly enhanced under the impact of geopolitical events. In addition, there is heterogeneity in the risk spillover between GPR and these three industries in different cycles. Notably, in the short term, the GPR is the main source of risk, and in the long term, the semiconductor industry has the great impact on GPR, 5G and rare earth industries, indicating that the semiconductor industry has very important geopolitical significance.

Bioavailability Improvement by Atomic Layer Coating: Fenofibrate A Case Study

Biopharmaceutical Classification Systems (BCS) class II drugs show poor solubility and high permeability in the body. Fenofibrate (FF) is a classic example of a BCS class II drug, used to treat high cholesterol and triglyceride (fat-like substances) levels in the blood. Atomic layer coating (ALC) is a surface engineering technology adapted from the semiconductor industry, where metal oxides are coated one atomic layer at a time over the active pharmaceutical ingredients (API) particles. ALC coating was proven to improve the processability, alter the hydrophilicity, improve the stability, and fine-tune the release of drugs. Herein, we report the intervention of ALC coating in enhancing the bioavailability of a poorly water-soluble drug (fenofibrate) in the animal model. The physical properties of uncoated fenofibrate were compared with those of zinc oxide-coated and silicon oxide-coated fenofibrate. Following the application of the coatings, the structural integrity (both chemical stability and solid-state stability) of the active pharmaceutical ingredient (API) remained uncompromised, as corroborated by 1H NMR and powder X-ray diffraction analyses. Notably, zinc oxide-coated fenofibrate exhibited favorable flow characteristics, whereas no discernible enhancement in flow behavior was observed for silicon oxide-coated fenofibrate. The results from contact angle measurements suggest that the silicon oxide-coated fenofibrate exhibits superior wetting behavior, as indicated by a contact angle nearing 0°. The application of ALC demonstrates an enhanced dissolution rate when compared to the uncoated active pharmaceutical ingredient (API) while leaving its equilibrium solubility unaffected. Coating the API with silicon oxide improves particle hydrophilicity and wetting properties, whereas zinc oxide coating aids in particle de-agglomeration, thereby enhancing their interaction with an aqueous medium. In vivo bioavailability studies conducted on rodents and larger animal (dog) models indicate a substantial increase in bioavailability (approximately 2 times) for the silicon oxide-coated API in comparison to the uncoated API, as determined by the area under the curve (AUC). Furthermore, the Cmax values for the silicon oxide-coated API also demonstrate a significant increase (approximately 3 times) over the uncoated API. Notably, an oral subacute toxicity study of ALC silicon-coated fenofibrate revealed no toxic effects attributable to the coating. This study underscores the potential of ALC in augmenting the bioavailability of BCS(II) drugs.

Theoretical study of particle and energy balance equations in locally bounded plasmas

In this study, new particle and energy balance equations have been developed to predict the electron temperature and density in locally bounded plasmas. Classical particle and energy balance equations assume that all plasma within a reactor is completely confined only by the reactor walls. However, in industrial plasma reactors for semiconductor manufacturing, the plasma is partially confined by internal reactor structures. We predict the effect of the open boundary area ( ) and ion escape velocity ( ) on electron temperature and density by developing new particle and energy balance equations. Theoretically, we found a low ion escape velocity ( / ) and high open boundary area ( ) to result in an approximately 38% increase in electron density and an 8% decrease in electron temperature compared to values in a fully bounded reactor. Additionally, we suggest that the velocity of ions passing through the open boundary should exceed under the condition .

Comprehensive Study of Low-Power SRAM Design Topologies

: The need for low power in portable and smart devices is the demand to be fulfilled for sustaining the semiconductor industry. Static Random Access Memory (SRAM) is the main part of the core design in chips. It is important to reduce the leakage power consumption during the steady mode of the device for the long run of the battery. This article is about the study of different modules using pre-existing low power. Application of different methods other than lowering the supply voltage leads to an increment in the number of transistors in conventional 6T (six transistor) SRAM cells like 7T to 14T. Power gating and the Multi-threshold complementary metal oxide semiconductor (MTCMOS) technique is the most relevant method. Hybrid low power techniques are in high demand because it shows better results than using individual techniques. However, the biggest challenge is to maintain the area and delay as well. FinFET came into the scenario to overcome the leakage power and short channel effect due to scaling in CMOS. Comparative study analysis shows that FinFET decreases the overall power and delay even when the number of transistors increases. A comparison was done between 6T, 8T, and 10T using FinFET and CMOS in a paper, and concluded that FinFET shows 77.792% improved write power.

Remote Work Dynamics in Semiconductor MNCs in Klang Valley: The Impact of Work Environment, Technological Infrastructure, and Work-Life Balance on Employee Productivity Moderated by Gender

The shift towards remote work has become a prominent feature of contemporary workplaces, driven by technological advancements and changing organizational paradigms. This study investigates remote work dynamics within semiconductor multinational corporations (MNCs) in Klang Valley, focusing on the interplay between work environment, technological infrastructure, work-life balance, and employee productivity. Social Exchange Theory (Blau, 1964), Operational model of analysis (Leitão et al., 2019) and Task-to-Performance Chain Theory (Goodhue and Thompson, 1995) guides the exploration of how these factors influence productivity, while Predictors and outcomes of LMX Model (Tziner et al., 2020) informs the examination of gender as a moderating factor. Utilizing a quantitative research approach, data was collected through structured surveys administered to employees of semiconductor MNCs in the Klang Valley region. The survey instrument captured key variables related to the work environment, technological infrastructure, and work-life balance. Statistical analyses, including regression modeling and moderation analysis, were employed to examine the relationships between these variables and productivity while considering gender as a moderating factor. The findings provide empirical evidence regarding the impact of the work environment, technological infrastructure, and work-life balance on employee productivity in remote settings within the semiconductor industry. Additionally, investigating gender as a moderating factor uncovers potential disparities in remote work experiences and outcomes between male and female employees. This study contributes to a deeper understanding of remote work dynamics, offering insights into organizational policies and practices to optimize productivity and foster inclusivity in remote work arrangements.

Predicting Work-in-Process in Semiconductor Packaging Using Neural Networks: Technical Evaluation and Future Applications

This review paper focuses on the application of neural networks in semiconductor packaging, particularly examining how the Back Propagation Neural Network (BPNN) model predicts the work-in-process (WIP) arrival rates at various stages of semiconductor packaging processes. Our study demonstrates that BPNN models effectively forecast WIP quantities at each processing step, aiding production planners in optimizing machine allocation and thus reducing product manufacturing cycles. This paper further explores the potential applications of neural networks in enhancing production efficiency, forecasting capabilities, and process optimization within the semiconductor industry. We discuss the integration of real-time data from manufacturing systems with neural network models to enable more accurate and dynamic production planning. Looking ahead, this paper outlines prospective advancements in neural network applications for semiconductor packaging, emphasizing their role in addressing the challenges of rapidly changing market demands and technological innovations. This review not only underscores the practical implementations of neural networks but also highlights future directions for leveraging these technologies to maintain competitiveness in the fast-evolving semiconductor industry.

The Impact of Economic Security Policy on Trade in Korea’s Semiconductor Industry: Between Korea, U.S., China, and Japan

The Impact of Economic Security Policy on Trade in Korea’s Semiconductor Industry: Between Korea, U.S., China, and Japan

Research of Gallium Nitride as Next-Generation Semiconductor

In today’s society, semiconductors have become an indispensable material in people’s lives, as they are closely related to daily needs such as internet access, power supply, and computing. The most widely used semiconductor material currently is silicon. Its cost advantage in the early stage made other semiconductor materials lag far behind. As semiconductor manufacturing has progressed to nanoscale and even 3-nanometer technology, problems with silicon semiconductors have become increasingly apparent. As the size of silicon transistors continues to shrink, quantum tunneling effects such as leakage current and gate leakage current have caused power consumption to increase continuously. Moreover, the silicon manufacturing process requires harsh conditions such as high vacuum, high temperature, and multiple steps, which increases the production cost and process complexity. Therefore, people have pinned their hopes on the next-generation semiconductor material, gallium nitride (GaN), as an alternative to silicon. Given its superior properties such as wider bandgap, higher electron mobility, and better thermal conductivity, gallium nitride (GaN) offers promising solutions to overcome the limitations of silicon-based semiconductors.

Advancements and Challenges in Photomask Technologies for Semiconductor Lithography: A Comparative Review of Binary, PhaseShift, and EUV Masks

This report investigates essential photomask technologies in semiconductor manufacturing and their developments. It outlines the principles and uses of binary photomasks, attenuated phase shift masks, and extreme ultraviolet (EUV) photomasks in lithography systems. With technological advancements, masks have evolved to tackle the demand for smaller feature sizes. Despite these advancements, each mask type has specific applications and constraints. The report also touches on other mask types, including ternary, chromeless phase lithography (CPL), and silicon-containing masks, and anticipates future challenges and directions in photomask technology

The Impact of the China-US Trade Friction on Vietnam’s Chip Industry

The impact of the China-US trade friction on the world has received widespread attention, but there is still insufficient focus on the Vietnamese chip industry. This article analyzes the impact of China-US trade on Vietnam and the background, development, challenges, and measures of Vietnam’s chip industry. This article argues that the China-US trade friction has brought development opportunities to Vietnam’s semiconductor industry. Many semiconductor companies have chosen to open factories and expand production in Vietnam, and Vietnam’s semiconductor market has also expanded. However, Vietnam has problems such as inadequate infrastructure, a weak technological foundation, government policies that are not suitable for foreign investment, and a lack of human resources. Based on this, this article proposes the following suggestions: Vietnam needs to actively seek cooperation with major countries, the government needs to play a coordinating and important investor role, Vietnam needs to strengthen infrastructure construction, Vietnam needs to promote technological innovation, and Vietnam needs to strengthen the training of high-end talents.

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.