Semiconductor Industry Research Articles

Low excess noise and high quantum efficiency avalanche photodiodes for beyond 2 µm wavelength detection

The rising concentration of greenhouse gases, especially methane and carbon dioxide, is driving global temperature increases and exacerbating the climate crisis. Monitoring these gases requires detectors that operate in the extended short-wavelength infrared range (~2.4 µm), covering methane (1.65 µm) and carbon dioxide (2.05 µm) wavelengths. Here, we present a high-performance linear mode avalanche photodetector (APD) with an InGaAs/GaAsSb type-II superlattice absorber and an AlGaAsSb multiplier, matched to InP substrates. This APD achieves a room temperature gain of 178, an external quantum efficiency of 3560% at 2 µm, low excess noise (less than 2 at gains below 20), and a small temperature coefficient of breakdown (7.58 mV/K·µm). These results indicate that a manufacturable semiconductor material-based APD could significantly advance high-sensitivity receivers for greenhouse gas monitoring, potentially enabling their commercial production and widespread use.

Optimization of Progressive Freeze Crystallization Process by Response Surface Methodology for the Treatment of Electroplating Wastewater for Copper (II) Sulphate Recovery

<p style="line-height:200%;text-align:justify;text-indent:0cm;text-justify:inter-ideograph;"><span style="line-height:200%;" lang="EN-US">For the copper-containing electroplating wastewater generated by a semiconductor company, the recovery of copper salts by progressive freeze crystallization and its process parameters were experimentally optimized and studied. Based on the results of single-factor experiments, according to the principles of Box-Behnken Design experimental design for response surface methodology to investigate the effects of coolant temperature, agitation rate, and freezing time on salt removal and optimize process parameters. The optimization results of the response surface method showed that the optimal conditions were when the coolant temperature was -8°C, the stirring rate was 264 r</span><span style="font-family:Symbol;line-height:200%;" lang="EN-US">×</span><span style="line-height:200%;" lang="EN-US">min<sup>-1</sup>, and the freezing time was 78 min. Three validation experiments were conducted under optimum conditions, and salt removal was obtained as 60.13 ± 4.10%, which is more in line with the predicted values. Treatment of copper-containing electroplating wastewater using tertiary freezing under response surface optimized experimental conditions. At this time, the total ice rate of the wastewater was about 65.00%, and the conductivity was also concentrated from 2.17 mS</span><span style="font-family:Symbol;line-height:200%;" lang="EN-US">×</span><span style="line-height:200%;" lang="EN-US">cm<sup>-1</sup> of the raw water to 2.99 mS</span><span style="font-family:Symbol;line-height:200%;" lang="EN-US">×</span><span style="line-height:200%;" lang="EN-US">cm<sup>-1</sup> with a total concentration ratio of 1.38. In addition, a higher concentration of concentrated liquid was obtained in the third freezing, while a crystalline salt precipitated from the bottom of the reactor. The precipitate was analyzed mainly as copper sulfate pentahydrate using XRD, XRF, EDS, and other characterization techniques.</span><span lang="EN-US"> Theoretical calculations show that pre-cooling of raw wastewater reduces energy consumption by up to 20.40%.</span></p>

Development of a virtual teaching module for advanced semiconductor fabrication and its learning effectiveness analysis

AbstractSemiconductor fabrication is the process of manufacturing semiconductor devices, typically integrated circuits (ICs) such as microprocessors and memory. This involves transferring circuit diagrams onto a silicon wafer using photomasks and photoresists. After a series of fabrication processes, ICs are created on the wafer surface and then diced into individual chips, which are packaged and tested with quality control procedures to become the final products. Virtual reality (VR) simulates imaginary experiences or environments difficult to achieve in the real world through human senses and immersive equipment, allowing users to interact in a virtual 3D space in real time, making it well suited for applications in science education and industrial training. This study transforms the essential knowledge of advanced semiconductor manufacturing processes into an easily understandable virtual teaching module, thereby creating educational resources for high school and college students. The objective is to enhance their scientific and technological literacy, yielding substantial benefits for the general public. This study utilized VR technology to simplify and clarify the knowledge about the semiconductor manufacturing process, making it more engaging for learners. The virtual teaching module's learning content includes an overview of wafer preparation, semiconductor fabrication, chip packaging, and IC testing. Users can interact with the virtual teaching module and conduct virtual experiments to enhance their understanding by trial and error. Experimental results show that it can improve students' learning achievement and learning motivation. Therefore, the virtual teaching module is suitable for high‐school students and the general public to understand semiconductor technology and its applications. The effectiveness of the virtual teaching module is heavily dependent on the availability and quality of VR hardware and software. Limited access to advanced VR equipment or technical issues could have affected the learning experience, thereby influencing the learning outcomes.

Uncertainty quantification on small angle x-ray scattering measurement using Bayesian deep learning

Small angle x-ray scattering (SAXS) is a widely recognized solution for measuring complex nanostructures. With the increasing demand for accurately assessing structural characteristics and optimizing manufacturing processes, uncertainty quantification in SAXS inverse problems has become a critical issue. However, traditional methods face challenges such as slow computation speed and inaccurate estimation of multidimensional parameters. To overcome these issues, we propose an uncertainty quantification approach suitable for SAXS measurement that approximates the posterior using Bayesian deep learning. The effectiveness and reliability of our method are illustrated by assessing structural parameters of synthetic 2D Si grating samples. The uncertainty quantification takes only about 2.3 s, thousands of times faster than the conventional Markov Chain Monte Carlo (MCMC) methods. Also, our method has superior repeatability for parameter measurement compared to the MCMC approaches. It provides the potential of efficient and reliable SAXS measurement in increasingly intricate semiconductor manufacturing.

Large-scale fabrication of meta-axicon with circular polarization on CMOS platform

Abstract Metasurfaces, consisting of arrays of subwavelength structures, are lightweight and compact while being capable of implementing the functions of traditional bulky optical components. Furthermore, they have the potential to significantly improve complex optical systems in terms of space and cost, as they can simultaneously implement multiple functions. The wafer-scale mass production method based on the CMOS (complementary metal oxide semiconductor) process plays a crucial role in the modern semiconductor industry. This approach can also be applied to the production of metasurfaces, thereby accelerating the entry of metasurfaces into industrial applications. In this study, we demonstrated the mass production of large-area meta-axicons with a diameter of 2 mm on an 8-inch wafer using DUV (Deep Ultraviolet) photolithography. The proposed meta-axicon designed here is based on PB (Pancharatnam–Berry) phase and is engineered to simultaneously modulate the phase and polarization of light. In practice, the fabricated meta-axicon generated a circularly polarized Bessel beam with a depth of focus (DoF) of approximately 2.3 mm in the vicinity of 980 nm. We anticipate that the mass production of large-area meta-axicons on this CMOS platform can offer various advantages in optical communication, laser drilling, optical trapping, and tweezing applications.

A Review Paper on Memory Fault Models and its Algorithms

The significance of testing semiconductor memories has grown significantly in the semiconductor industry due to the increased density of modern memory chips. This paper aims to investigate and analyze different types of functional faults present in today's memory technology. These faults include stuck-at faults, transition faults, coupling faults, address decoder faults, and neighborhood pattern-sensitive faults. The paper also delves into the techniques utilized to identify and detect these faults. In particular, the focus is placed on the importance of zero-one, checkerboard, and March pattern tests, which are widely employed to assess functional memory defects at different levels, such as the chip level, array level, and board level. Furthermore, the study provides an in-depth exploration of various test algorithms and thoroughly examines their fault coverage capabilities. Overall, this review paper provides valuable insights into the challenges posed by the dense nature of modern memory chips and offers a comprehensive analysis of functional faults in memory technology. By emphasizing the importance of testing and presenting a detailed exploration of fault detection methods and test algorithms, this study contributes to the advancement of reliable and high-performance memory devices in the electronic industry suggesting that MARCH algorithms outperform others when considering factors like fault coverage, power efficiency, area optimization, and time complexity parameters, making them the preferable choice for reliable and high-performance memory devices in the electronic industry.

Piezoelectric Shunt Damping for a Planar Motor Application under Cryogenic Conditions

For several decades, Moore’s law has driven the semiconductor industry, with computational power and production costs as the main drivers. Such drivers have enabled several technological innovations in the mechatronics and dynamics architecture of photolithography machines, used for semiconductor circuits manufacturing. Among current investigations, the use of superconductive magnets would enable higher accelerating stages and, thus, higher throughput and lower manufacturing costs. However, this involves a complex magnet structure that needs to operate at cryogenic temperatures and mechanical resonances at relatively low frequencies as a result of the thermal architecture of the system. The damping options are also limited due to the very low temperature. This paper explores the use of shunted piezoelectric transducers for damping the internal modes of the magnet mass. A classical resistive and inductive RL shunt is considered. The study was conducted both numerically and experimentally on a demonstrator of a superconductive magnet plate concept, where piezoelectric transducers are incorporated to support the superconducting coils. The study demonstrates the effectiveness of piezoelectric shunts as a damping solution at very low temperatures, with limited impact of the temperature variation on the performance.

Insight into the responses of the anammox granular sludge system to tetramethylammonium hydroxide (TMAH) during chip wastewater treatment

Tetramethylammonium hydroxide (TMAH), an extensively utilized photoresist developer, is frequently present in ammonium-rich wastewater from semiconductor manufacturing, and its substantial ecotoxicity should not be underestimated. This study systematically investigated the effects of TMAH on the anammox granular sludge (AnGS) system and elucidated its inhibitory mechanisms. The results demonstrated that the median inhibitory concentration of TMAH for anammox was 84.85 mg/L. The nitrogen removal performance of the system was significantly decreased after long-term exposure to TMAH (0–200 mg/L) for 30 days (p < 0.05), but it showed adaptability to certain concentrations (≤50 mg/L). Concurrently, the stability of the granules decreased dramatically, resulting in the breakdown of AnGS. Further investigations indicated that TMAH exposure increased the secretion of extracellular polymeric substances but weakened their defense function. The increase in reactive oxygen species resulted in damage to the cell membrane. Reduced activity of anammox bacteria, impeded electron transfer, and changes in enzyme activity suggested that TMAH affected the metabolic activity. Microbiological analysis revealed that TMAH caused a decrease in the abundance of anammox bacteria and a weakening of symbiotic interactions within the microbial community. These results provide valuable guidance for the AnGS system application in chip wastewater treatment.

A sustainable semiconductor supply chain under regulation

Modern life would not exist without semiconductors as all electronic components used in computers, telecommunications, health care, transportation, and energy systems are equipped with chips. To examine both backward and forward activities in semiconductor industry, this paper formulates the industry as a closed-loop supply chain. It articulates how old semiconductors are processed and recycled to manufacture new silicon and chips, and examines the impact of a commonly applied subsidy scheme on the performance of semiconductor firms which operate in upstream and downstream layers of the industry. Specifically, the proposed semiconductor supply chain involves (i) a return function sensitive to monetary incentives; (ii) a subsidy legislation rewarding end-users for recycling; (iii) upstream industry where silicon is produced using virgin and scrap materials; (iv) downstream industry in which semiconductor manufacturers (such as TSMC, Samsung, Intel) buy silicon and other materials, hire workers, and then produce and sell chips. We characterize Stackelberg equilibrium silicon and semiconductor prices and outputs and calibrate model parameters using actual data to quantify the effects of subsidy and collection channels on silicon and semiconductor firms’ performance. We find that the subsidy scheme neither distorts firms’ strategies nor causes any inefficiency for the semiconductor industry. It stimulates circular economy activities and provides economic and environmental benefits.

Single-crystal hBN Monolayers from Aligned Hexagonal Islands

Hexagonal boron nitride (hBN), as one of the few two-dimensional insulators, holds strategic importance for advancing post-silicon electronic devices and circuits. Achieving wafer-scale, high-quality monolayer hBN is essential for its integration into the semiconductor industry. However, the physical mechanisms behind the chemical vapor deposition (CVD) synthesis of hBN are not yet well understood. Investigating morphology engineering is critical for developing scalable synthetic techniques for the large-scale production of high-quality hBN. In this study, we explored the underlying mechanisms of the CVD growth process of hBN and found that the involvement of a small amount of oxygen effectively modulates the shape of the single-crystal hBN islands. By tuning the oxygen content in the CVD system, we synthesized well-aligned hexagonal hBN islands and achieved a continuous, high-quality single-crystal monolayer hBN film through the merging of these hexagonal islands on conventional single-crystal metal-foil substrates. Density functional theory was used to study the edges of hBN monolayers grown in an oxygen-assisted environment, providing insights into the formation mechanism. This study opens new pathways for controlling the island shape of 2D materials and establishes a foundation for the industrial-scale production of high-quality, large-area, single-crystal hBN.

The impact of big fund on the operational efficiency of semiconductor companies: Corporate social responsibility as a non-economic output

This study contributes to research on the total factor operational efficiency of the semiconductor company considering corporate social responsibility (CSR) by proposing a novel integer-valued nonparametric frontier method. This method adopts an endogenous directional distance function, which first selects the benchmark and then endogenously determines the directional vector. Avoiding the issue of non-integer slack adjustment increases the convenience of formulating incentive plans and calculating efficiency. We apply this method to the input–output data of 185 listed Chinese semiconductor companies from 2010 to 2022, considering integer-valued CSR as non-economic output. Global Malmquist productivity index analysis shows that the average total factor operational efficiency of companies that received investment from the National Integrated Circuit Industry Investment Fund (Big Fund) increased by 6.9%. Using the econometric technique, we verify that the total factor operational efficiency of semiconductor companies receiving Big Fund investment increased by 0.084 compared to those without Big Fund investment. Heterogeneity analysis showed that the effect of Big Fund was more significant in improving the total factor operational efficiency of state-owned, new, and non-integrated circuit semiconductor companies, implying the necessity of purposeful investment policies for the Big Fund.

Effect of filer content on the thermal characteristics of underfill materials for Ball-Grid-Array component package

High-density integration and fast processing speed in the semiconductor industry have increased heat generation in electronic devices. Underfill materials, known for their high thermal conductivity and low coefficient of thermal expansion (CTE), offer a potential solution to dissipate heat and improve device reliability. In this study, we investigate the effect of filler content on the thermal reliability of underfill materials for ball grid array (BGA) component packaging. Mainly, we investigate the thermal conductivity, CTE, and mechanical properties of different filler contents of Al2O3 and Al2O3–BN hybrid underfills to facilitate heat dissipation and improve device reliability. The thermal conductivity of the underfill materials was evaluated by measuring the surface temperatures of underfill molded flip-chip light-emitting diodes (FCLEDs). The mechanical properties and thermo-mechanical reliability of the underfill materials were evaluated via a three-point bending test of the underfill packaged BGA components after the thermal shock test. The results showed that optimizing underfill properties based on specific application environments is crucial for obtaining enhanced thermal reliability and mechanical properties of underfill packaged BGA components.

Rotary ultrasonic surface machining of silicon: Effects of ultrasonic power and tool rotational speed

The surging demand for monocrystalline silicon materials in the production of microelectronic components highlights its crucial role in the semiconductor and optic industries. Hence it is inevitable to produce a silicon workpiece with high quality finish to meet the demand in semiconductor industries. Due to high brittleness, controlling the quality of silicon in surface machining is quite difficult. Traditional manufacturing processes induce issues like rough surfaces and edge chipping. It was reported that rotary ultrasonic surface machining (RUSM) can effectively reduce cutting force, roughness, and edge chipping in machining of brittle materials. There have been several studies on drilling and sliding silicon materials using rotary ultrasonic machining investigating the effects of machining parameters on the output variables such as cutting force, torque, edge chipping, surface roughness etc. However, to the best of the authors’ knowledge, there are no reported investigations on effects of machining variables (ultrasonic power and tool rotation speed) in surface machining of silicon materials using the rotary ultrasonic machining. This study aimed to investigate the impacts of ultrasonic power and tool rotation speed on the cutting force, edge chipping, and surface roughness. Experimental results show that the ultrasonic vibration and tool rotation speed had a notable impact on edge chipping and cutting forces. Lastly, the current research has paved the way for widening the research on investigating grinding of the silicon wafer in semiconductor manufacturing with ultrasonic vibration and high rotation speed. In semiconductor wafer manufacturing, grinding process is used to reduce the flatness but generate surface and subsurface damage. With further investigations, RUSM can contribute to reducing these damages.

U.S.-China Trade War: Analysis Of The Semiconductor Industry

This research paper analyses the causes and outcomes of the U.S.-China trade conflict through the lens of the semiconductor industry. It explores strategic measures the U.S. took to curb China’s technological progress. Furthermore, the paper looks at China’s countermeasures to mitigate the repercussions of U.S. policies. This research paper is a literature review of various research papers to unveil the causes and effects of the trade war, in the semiconductor industry. Some of the leading causes include intellectual property disputes(Huawei and ZTE) and trade relations, the outcomes of the trade war are more directed toward economic competition and global impact. The paper proposes potential policies to mitigate the damages caused by the trade war and recommends policies to create a robust economic environment. The paper explores policies that could enhance diplomatic relations and minimize trade wars. Methodology The research methodology used in this paper is a Literature review. The five main papers reviewed are... 1. “What the Huawei Case Can Teach Us about the U.S.-ChinaPower Game” by Lu Chuanying and Nicolas Huppenbauer. 2. “The U.S.-China Trade War: Is Malaysia benefiting from "Diversions" in US import demand?” by Calvin Cheng, Firdaos Rosli, and Dwintha Maya Kartika. 3. “Economic Security: Uncertainty Rises as U.S.-China Strategic Competition Deepens” by Yeon Wonho. 4. “U.S.-China Bilateral Economic Relations” by Amy P. Celico and Song Guoyou. 5. “The India-China-U.S. Triangle” by Vijay Gokhale. These research papers are analyzed to highlight some of the chief factors in the semiconductor industry that led to the trade war, as well as investigate papers to comprehend the outcomes of the trade war on the semiconductor industry

Biostability assessment of ultrapure water piping materials in the semiconductor industry and their susceptibility to Ralstonia growth

The pipes used for ultrapure water (UPW) distribution are constructed using high-quality materials to ensure purity. However, there remains a risk of contamination based on the pipe type. This study is the first to comprehensively evaluate the biological implications of various UPW pipe materials used in the semiconductor industry. The results showed that Polyvinylidene fluoride (PVDF) exhibited lower organic carbon leaching (0.08 mg/L) and a reduced biomass formation potential (BFP) (approximately 5 × 105 cells/cm2) compared to chlorinated polyvinyl chloride (CPVC) pipes. In particular, Ralstonia, an oligotrophic bacterium commonly found in UPW systems, formed a significant biofilm on pipe surfaces, notably in stainless steel (SUS) and CPVC pipes. Furthermore, particle contamination, a critical concern in semiconductor manufacturing, was investigated, focusing on potential contamination sources generated by pipe leaching and the presence of bacteria. The bacterial composition of the selected UPW was investigated, revealing Herbaspirillum, a nitrogen-fixing bacterium, as the dominant species, account for 66.15 %. Notably, the composition of the feedwater was different from that of the UPW. This study also highlights the limitations of culture-based methods, particularly in detecting bacteria under oligotrophic conditions, which are often unculturable. Flow cytometry (FCM) shows promise for the quick detection of bacterial contamination by providing total cell counts. Moreover, cytometric fingerprinting analysis revealed phenotypic differences between the communities. Nevertheless, further development of a simplified and widely applicable cell counting protocol is required for its effective integration into UPW production.

Speed of sound for understanding metals in extreme environments

Knowing material behavior is crucial for successful design, especially given the growing number of next-generation energy, defense, and manufacturing systems operating in extreme environments. Specific applications for materials in extreme environments include fusion energy, semiconductor manufacturing, metal additive manufacturing, and aerospace. With increased applications, awareness of foundational science for materials in extreme environments is imperative. The speed of sound provides insights into phase boundaries, like shock-induced melting. Thermodynamic integration of the speed of sound enables the deduction of other desirable properties that are difficult to measure accurately, like density, heat capacity, and expansivity. Metrology advancements enable the speed of sound to be measured at extreme conditions up to 15 000 K and 600 GPa. This comprehensive review presents state-of-the-art sound speed metrology while contextualizing it through a historical lens. Detailed discussions on new standards and metrology best practices, including uncertainty reporting, are included. Data availability for condensed matter speed of sound is presented, highlighting significant gaps in the literature. A theoretical section covers empirically based theoretical models like equations of state and CALPHAD models, the growing practice of using molecular dynamics and density functional theory simulations to fill gaps in measured data, and the use of artificial intelligence and machine learning prediction tools. Concluding, we review how a lack of measurement methods leads to gaps in data availability, which leads to data-driven theoretical models having higher uncertainty, thus limiting confidence in optimizing designs via numerical simulation for critical emerging technologies in extreme environments.

Mitigation of flow-induced vibrations in high-speed flows using triply periodic minimal surface structures

Flow-induced vibrations (FIVs) pose challenges and limit intended functionalities in many industrial sectors, especially in high-tech industries such as semiconductor manufacturing. Because of extreme precision requirements, any mechanical perturbation to the manufacturing process detrimentally affects production quality and process yield. The impact of FIV must, thus, be minimized. This study investigates the FIV reducing properties of triply periodic minimal surface (TPMS) inserts in industrial cooling systems by expanding the Darcy–Forchheimer model for flow through porous media to a turbulent regime. Using the expanded model, we performed full-scale finite-element simulations for high-speed flows in a pipe with an abrupt expansion of diameter and analyzed the effects of TPMS inserts on the reduction of induced turbulence and FIV. Our data revealed that the TPMS inserts exhibit promising characteristics for FIV mitigation revealing up to 97.6% reduction in turbulent kinetic energy and 32.7% reduction in vorticity. These results complement available data on using TPMS inserts to eliminate geometry-induced instabilities in internal pipe flows.

Der Standort Österreich aus Sicht der ökonomischen Komplexität

Abstract Economic complexity indicators attempt to map latent factors for long-term growth. In this analysis, we shed light on Austria as a business location using multidimensional complexity indicators that allow statements to be made about regional specialization, key technologies for the green transition and individual sectors such as the semiconductor industry. Austria usually occupies top international positions or is at least among the leaders, and this has remained stable over the last 20 years. This implies extensive opportunities for diversification into promising technologies. For individual brown industries, however, there is a risk of getting locked into products whose demand will decline in the course of the green transition.

Industrial data-driven machine learning soft sensing for optimal operation of etching tools

Smart Manufacturing, or Industry 4.0, has gained significant attention in recent decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As modern production methods continue to increase in complexity, there is a greater need to consider what variables can be physically measured. This advancement necessitates the use of physical sensors to comprehensively and directly gather measurable data on industrial processes; specifically, these sensors gather data that can be recontextualized into new process information. For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. However, a key challenge is the availability of high quality data that covers the full operating range, including the day-to-day variance. This paper examines the applicability of soft sensing methods to the operational data of five industrial etching machines. Data is collected from readily accessible and cost-effective physical sensors installed on the tools that manage and control the operating conditions of the tool. The operational data are then used in an intelligent data aggregation approach that increases the scope and robustness for soft sensors in general by creating larger training datasets comprised of high value data with greater operational ranges and process variation. The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. For PASS/FAIL classification, a data aggregation method is developed to enhance model predictive performance with larger training datasets. A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. For large datasets with high quality data that enable model training for more complex tasks, regression models that predict the oxide thickness of the product are also developed. Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. Both the classification and regression models can be applied in industrial settings as they provide additional information regarding the process outcome. Individually, these models can reduce the number of metrology steps in semiconductor factories, and when developed further, can empower the development of advanced process control strategies.

Novel and practical fabrication of pre-seasoned Y2O3 ceramics through surface modification

In the semiconductor industry, the long “seasoning” time required after the installation of new ceramic components in etching equipment restricts the wafer production efficiency. This study proposes a novel and practical method for the fabrication of pre-seasoned ceramics through surface modification of Y2O3 ceramics. Fluorine-containing reactive vapors were produced by utilizing NH4F as the source. Further, the formation of yttrium oxyfluoride layers on the surfaces of Y2O3 ceramics was demonstrated. The modified surface comprises a single-crystalline phase near the interface with the Y2O3 substrate. The change in the composition, crystalline nature of the modified layer, and orientation relationship with the substrate were examined via transmission electron microscopy. The kinetics of the surface modification were studied for the sintered Y2O3 bulk as well as for practical coatings such as plasma-sprayed or PVD coatings. The proposed surface modification approach can be used to fabricate pre-seasoned Y2O3 coatings, which can significantly benefit the semiconductor industry.