Semiconductor Industry Research Articles

Gas viscosities from a semiconductor-manufacturing mass-flow controller

We obtain gas viscosities with a pressure-based mass-flow controller used for semi-conductor manufacturing, a rate-of-change approach, and a physics-based calculation. The novelty of this method is that it is used in an industrial process whose main goal is not to measure viscosities. In this way, for pressures of the order of 10 kPa and 25 ∘C and 35 ∘C, we obtain for seven gases viscosities with mean absolute errors with respect to reference viscosities of less than 1%. Using this method, we report viscosities for two semiconductor-manufacturing gases not available in the open literature: hexafluoroisobutene (CAS # 382-10-5) and 1,1,3,3,3-pentafluoropropene (690-27-7).

In-situ particle analysis with heterogeneous background: a machine learning approach

We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle–substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle–substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing.

Porphyrin Aluminum Metal-Organic Framework in Liquid Water, its Interaction with the Oxidized Organosulfur Compound Diethyl Sulfoxide, and its Sorption from Aqueous Solution.

Oxidized organosulfur compounds and, in particular, sulfoxides are of interest as solvents in the semiconductor and pharmaceutical industry, environmental contaminants, and simulants in deactivation of chemical warfare agents. An experimental study is reported of the interaction of porphyrin aluminum metal-organic framework Al-MOF-TCPPH2 (Compound 2) with diethyl sulfoxide (DESO) in pure form and in aqueous solution. First, the suitability of Compound 2 as sorbent in aqueous solution was assessed; namely, its long-term stability (up to 15 days) in liquid water has been investigated at room temperature and under stirring. Here, a novel facile spectroscopic method has been used, a periodic micro-sampling of sorbent from suspension, followed by vacuum mini-filtration and an ex situ time-dependent attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) analysis. Next, the interaction of Compound 2 with pure liquid DESO under ambient conditions was investigated, which yields the stoichiometric adsorption complex (Al-MOF-TCPPH2)1(DESO)2 denoted Compound 3. In this adsorption complex, molecules of DESO interact with the OH group and carboxylate group of the sorbent. Then, the removal of DESO from Compound 3 was assessed, using facile treatment with warm water in the micro Soxhlet apparatus followed by the ATR FT-IR analysis. Finally, Compound 2 was tested in sorption of DESO from diluted aqueous solution. In the initial step, the sorption proceeds very quickly (in <1 min the concentration of DESO decreases by about 20%) followed by a much slower step. The maximum amount of adsorbed DESO corresponds to half of the amount adsorbed from pure DESO as found by the high-performance liquid chromatography-ultraviolet detection method. This adsorbed amount corresponds to 1 mol DESO adsorbate per mol of sorbent. Porphyrin aluminum metal-organic framework Compound 2 is promising for the removal of DESO from diluted aqueous solution, and it is of interest for the removal of similar oxidized organosulfur compounds.

Effect of horizontal magnetic field position on oxygen distribution in CZ silicon crystal growth

The horizontal magnetic field-assisted Czochralski(CZ) method is gaining significant popularity in the semiconductor industry for producing 200 mm semiconductor-grade silicon wafers. In this study, the impact of the horizontal magnetic field position on the oxygen content and radial uniformity of semiconductor-grade silicon crystals produced by the CZ method was investigated. Three-dimensional simulations were conducted using finite element software to calculate the melt convection and oxygen content before and after adjustment of the magnetic field position, respectively. The simulations results predicted a decrease in the oxygen content of the crystals following the adjustment of the magnetic field position from 0 to 75 mm below the free surface, and this was experimentally verified. The velocity distribution of the free melt surface exhibits better uniformity when lowering the magnetic field position. The influence of the magnetic field position on the temperature distribution and Marangoni flow in the melt was investigated, and the result of oxygen concentration difference identified the adjustment of the magnetic field position promoted the greater Marangoni flow below the free melt surface, which promoted the volatilization of SiO gas and thus reduced the oxygen concentration in the silicon ingot. Therefore, adjusting the position of the magnetic field is an effective way to increase the Marangoni flow and to obtain semiconductor-grade silicon crystal with lower oxygen content and better oxygen radial homogeneity.

Parameter free AEWMA control chart for dispersion in semiconductor manufacturing

The study presents a new parameter free adaptive exponentially weighted moving average (AEWMA) control chart tailored for monitoring process dispersion, utilizing an adaptive approach for determining the smoothing constant. This chart is crafted to adeptly detect shifts within anticipated ranges in process dispersion by dynamically computing the smoothing constant. To assess its effectiveness, the chart’s performance is measured through concise run-length profiles generated from Monte Carlo simulations. A notable aspect is the incorporation of an unbiased estimator in computing the smoothing constant through the suggested function, thereby improving the chart’s capability to identify different levels of increasing and decreasing shifts in process dispersion. The comparison with an established adaptive EWMA-S2 dispersion chart highlights the considerable efficiency of the proposed chart in addressing diverse magnitudes of process dispersion shifts. Additionally, the study includes an application to a real-life dataset, showcasing the practicality and user-friendly nature of the proposed chart in real-world situations.

Significance of chemical reaction and viscous dissipation on magnetized-Marangoni convective flow of dusty Casson hybrid (AA7072+AA7075/sodium alginate) nanofluid with Soret and Dufour effects

ABSTRACT In the current study, mass and heat transfer flow of radiated Casson hybrid nanofluid with nanoparticles and dust deferment over sheet are explored. The Soret and Dufour effects, activation energy and microorganisms in the hybrid nanofluid model are described. Marangoni convection, a prominent occurrence in microgravity caused by surface tension gradients. It can be used for many different things, such as the creation of molten crystals, thin-film diffusion, the generation of vapour bubbles during nucleation, and semiconductor manufacturing. In sodium alginate, we wondered about the aluminium alloys AA7072 and AA7075. The aluminium alloys included in this study are specially made materials with improved heat transmission characteristics. aluminium and zinc are combined in AA7072 alloy in the ratios of 98 & 1, respectively, with the addition of silicon, ferrous metals, and copper. Similarly, AA7075 is a blend of aluminium, magnesium, zinc, and copper in the proportions of ~ 90, ~3, ~6, and ~ 1, respectively, with silicon ferrous and magnesium as additional metals. The controlling PDEs are transformed into nonlinear ODEs with the aid of a new set of similarity variables. These ODEs are then numerically solved using the RKF-45th method. The findings show that when the Marangoni convection parameter increases, the concentration, temperature, and microbiological characteristics drop while the velocity profile increases for both the dust and fluid phases.

DFT and TD‐DFT Study of Many‐Body and Hydrogen Bonding Interactions: Nitrosamine Oligomers

AbstractThis study focuses on the properties and intermolecular interactions of nitrosamine oligomers, which are important in both semiconductor manufacturing and nuclear waste management. The study explores various configurations of nitrosamine oligomers, ranging from monomers to pentamers in linear, ladder, and cyclic clusters, with the finding that dimer cyclic forms and hexamers and above are unstable. Many‐body analysis revealed that two‐body interaction energies made remarkable contributions to the binding energy, while the sum of 3‐, 4‐, and 5‐body energy contributed less, indicating the decrease in stability of nitrosamine from dimer to pentamer clusters. Using advanced computational techniques, such as dispersion‐corrected B3LYP‐D3 and TD‐DFT, the study investigates the vibrational modes and electronic absorption spectra of nitrosamine oligomers. The results reveal that different configurations can significantly affect the vibrational modes and electronic properties of nitrosamine oligomers. The NH2 symmetric and asymmetric modes were particularly intense in various forms of nitrosamine oligomers. The wavelengths of electronic transition, oscillator strength, and HOMO to LUMO gap were reported. Additionally, the study reports on nitrosamine oligomers’ hydrogen‐bonded cooperativity and binding energy contributions. Overall, these findings offer valuable insights into the stability and properties of nitrosamine oligomers, with potential applications in materials science and chemical engineering.

Advancements And Applications of Finfet Technology in Modern Semiconductor Engineering

This comprehensive study presents an in-depth analysis of Fin Field-Effect Transistor (FinFET) technology, a significant innovation in semiconductor technology, focusing on its structural attributes, applications, and future prospects. FinFETs, characterized by their distinctive fin-like channels, are pivotal in overcoming the limitations of traditional MOSFETs, particularly in the context of device miniaturization. The paper begins by exploring the basic theory and structural characteristics of FinFETs, emphasizing their enhanced control over short channel effects and reduced leakage currents in scaled-down devices. Attention is then directed towards the integration of FinFET technology in Static Random-Access Memory (SRAM), detailing how FinFETs contribute to improved stability and efficiency in memory storage. Another crucial application discussed is in the biomedical field, where FinFETs have facilitated the development of advanced instrumentation amplifiers and sensitive sensors, marking a leap in medical electronics. The paper concludes by addressing the challenges encountered by FinFETs as fabrication approaches the 5-7nm scale, including their physical and structural limitations. It also highlights ongoing research in structural modifications and material innovations like Gate-All-Around FETs and alternative materials, underscoring the continued evolution and relevance of FinFET technology in the ever-advancing semiconductor industry.

The growth patterns of firms in the industrial value chain using the hidden markov model: Evidence from the Korean semiconductor industry

Having steadily grown over the last 40 years, the semiconductor industry in South Korea has emerged as one of the leading industries in the world. However, it has recently entered a period of great change because of the volatility in the global value chain caused by trade conflicts and protectionism in the pursuit of technological sovereignty by countries. To cope with these changes, the South Korean government has endeavoured to strengthen the competitiveness of domestic materials and equipment segments, which are the backbone of the semiconductor industry. Nevertheless, most previous studies on the semiconductor industry in South Korea focused on the downstream of the value chain (which means the manufacturing of finished components). However, only a few attempts were made to investigate the upstream part of the industry, that is, materials and equipment segments. Therefore, this study analyses the growth patterns of firms engaged in the Korean semiconductor industry value chain, including finished components, materials, and equipment. For this objective, we applied the Hidden Markov Model to time-series sales data collected from 1998 to 2019 for 1,259 firms in the value chain. The analysis results indicated that the growth of these firms could be modelled using six stages, based on which nine patterns of growth were identified. This study proposes a novel data-driven approach for tracking firm growth across the domestic industry value chain from a longitudinal perspective. Thus, we provide valuable policy insights for nurturing the domestic industry’s value chain.

Concurrent delamination propagation and deformation localization in semiconductor devices

In semiconductor devices, metals and polymers are often integrated as electrical conductors and insulators. Fabrication and operation of the devices cause stress in such an integrated structure. The stress can delaminate the metals and polymers, as well as deform them plastically. Here we show that the delamination can localize the deformation in the metals, which form necks to rupture the metals and sever electrical conduction. We simulate the coevolution of delamination propagation and deformation localization. The metals and polymers are modeled as elastic-plastic materials, and their adhesion is represented by a cohesive zone model. The simulation highlights that extensive delamination is a prerequisite for the metals to form necks, and that necking causes delamination to propagate further. Also highlighted are the impact of corners in the structure. Preexisting defects make the structure particularly vulnerable to concurrent delamination and necking. It is hoped that this study will draw renewed attention to the mechanical behavior of materials in the development of the semiconductor industry.

Enhancing Carrier Mobility in Monolayer MoS2 Transistors with Process-Induced Strain.

Two-dimensional electronic materials are a promising candidate for beyond-silicon electronics due to their favorable size scaling of electronic performance. However, a major challenge is the heterogeneous integration of 2D materials with CMOS processes while maintaining their excellent properties. In particular, there is a knowledge gap in how thin film deposition and processes interact with 2D materials to alter their strain and doping, both of which have a drastic impact on device properties. In this study, we demonstrate how to utilize process-induced strain, a common technique extensively applied in the semiconductor industry, to enhance the carrier mobility in 2D material transistors. We systematically varied the tensile strain in monolayer MoS2 transistors by iteratively depositing thin layers of high-stress MgOx stressor. At each thickness, we combined Raman spectroscopy and transport measurements to unravel and correlate the changes in strain and doping within each transistor with their performance. The transistors displayed uniform strain distributions across their channels for tensile strains of up to 0.48 ± 0.05%, at 150 nm of stressor thickness. At higher thicknesses, mechanical instability occurred, leading to nonuniform strains. The transport characteristics systematically varied with strain, with enhancement in electron mobility at a rate of 130 ± 40% per % strain and enhancement of the channel saturation current density of 52 ± 20%. This work showcases how established CMOS technologies can be leveraged to tailor the transport in 2D transistors, accelerating the integration of 2D electronics into a future computing infrastructure.

Optimization of bulk microdefect performance in epitaxial silicon wafer

Bulk microdefects (BMDs) in epitaxial silicon wafers are pivotal for advanced node integrated circuits, offering enhanced mechanical strength and metal gettering capabilities. This study introduces two heat treatment procedures to optimize BMD density and radial uniformity in lightly doped wafers, addressing the challenges associated with large-diameter crystal growth and the thermal budget constraints of advanced node processes. We demonstrate that Rapid Thermal Annealing (RTA) at temperatures exceeding 1175 °C significantly improves BMD uniformity. A post-RTA stabilization step, when performed prior to epitaxial growth, enhances BMD density by retaining nuclei generated by RTA. Conversely, when the RTA with stabilization step follows epitaxial growth, a higher BMD density and larger BMD size are achieved. Light Scattering Tomography (LST) analysis confirms the optimal conditions for these treatments. The findings contribute to the semiconductor industry by optimizing wafer properties for high-performance ICs.

Wire Bow In Situ Measurement for Monitoring the Evolution of Sawing Capability of Diamond Wire Saw during Slicing Sapphire.

Electroplated diamond wire sawing is widely used as a processing method to cut hard and brittle difficult-to-machine materials. Currently, obtaining the sawing capability of diamond wire saw through the wire bow is still difficult. In this paper, a method for calculating the sawing capability of diamond wire saw in real-time based on the wire bow is proposed. The influence of the renewed length per round trip, crystal orientation of sapphire, wire speed, and feed rate on the wire sawing capability has been revealed via slicing experiments. The results indicate that renewing the diamond wire saw, and reducing the wire speed and feed rate can delay the reduction in sawing capability. Furthermore, controlling the value of renewed length per round trip can make the diamond wire saw enter a stable cutting state, in which the capability of the wire saw no longer decreases. The sawing capability of diamond wire saw cutting in the A-plane of the sapphire is smaller than that of the C-plane, and a suitable feed rate or wire speed within the range of sawing parameters studied in this study can avoid a rapid decrease in the sawing capability of the wire saw during the cutting process. The knowledge obtained in this study provides a theoretical basis for monitoring the performance of the wire saw, and guidance for the wire cutting process in semiconductor manufacturing. In the future, it may even be possible to provide real-time performance parameters of diamond wire saw for the digital twin model of wire sawing.

Deep ensemble learning for material removal rate prediction in chemical mechanical planarization with pad surface topography

Chemical mechanical planarization (CMP) stands as a critical process in semiconductor manufacturing, necessitating precise prediction of material removal rate (MRR) to achieve optimal global planarization of material surface step heights. A notable hurdle in MRR prediction lies in the nonlinear pad-wafer interaction stemming from variations in the polishing pad surface. Despite numerous extant studies examining the asperity of the pad surface and the wafer-pad contact, aspects such as slurry lubrication and micro-topographical structure of the pad surface remain unaddressed. This study firstly proposes a deep ensemble learning-based approach for MRR prediction, leveraging the intricate interactions between the overall features of the pad surface and the polishing pressure. We experimentally generated diverse pad surfaces by controlling the process variables governing pad surface topography. The Multi-TabNet model, constructed by independently training multiple TabNet instances with distinct initialization and hyperparameters, furnishes a framework for MRR prediction across a broad spectrum of pad surface features, encompassing pores and cores. The results demonstrate a root mean square error of 80 Å/min, with the error margin reduced by up to 70 % compared to predictions utilizing a single parameter of the pad surface. Furthermore, it exhibits an error improvement exceeding 17 % relative to the existing method solely considering asperity. This study not only imparts insights for addressing MRR variations contingent on pad condition but also facilitates dependable material removal even on variable pad surfaces due to conditioner wear.

Multi-filament ion source for uniform ion beam generation

Ion beams are employed in various fields such as semiconductor manufacturing, surface modification and material science. The uniformity of ion beams is crucial in many applications, but conventional ion sources that use a single filament often limit the uniformity and intensity of the ion beam. This paper presents a study that aims to optimize a multi-filament ion source to enhance the uniformity of ion beams. The study includes a detailed explanation of the ion source components and design, methods for measuring ion beam uniformity with its experimental design, followed by results, analysis, discussions and conclusions, completed by suggestions for future research directions. The experimental results demonstrate that the use of a multi-filament ion source improves ion beam uniformity compared to a single-filament ion source. An optimal design for the ion source components and new approaches for improving ion beam uniformity are described. The study’s results provide important information for improving ion beam uniformity and offer a technical basis for providing high-quality products and services in various industries.

Effects of Gas-Dissolved Water for Ceria Nanoparticles on the SiO2 Film Surface in Post-CMP Cleaning

As a trend of using colloidal and smaller ceria nanoparticles (CNPs) at the shallow trench isolation (STI) chemical mechanical polishing (CMP) in semiconductor manufacturing, post-CMP cleaning challenges in the removal of residual CNPs on the SiO2 film surface became much more challenging. We investigated the reduction/oxidation of ceria nanoparticles (CNPs) by hydrogen gas-dissolved water (H2 GDW), carbon dioxide gas-dissolved water (CO2 GDW), and oxygen gas-dissolved water (O2 GDW). The concentration of Ce3+ on the CNPs changed from 18.64% to 19.48%, 20.31% to 21.94%, and 21.27% to 19.22%, respectively, after immersion in H2 GDW, CO2 GDW, and O2 GDW for 3 to 12 h. Following the Ce3+ concentration of the CNP surface, adhesion energies between CNPs immersed in H2 GDW, CO2 GDW, and O2 GDW for 6 h with SiO2 surface were 6.06E-16 J, 6.18E-16 J, and 4.83E-16 J. Cleaning experiments under megasonic conditions revealed the efficacy of H2 GDW and O2 GDW in removing residual CNPs from SiO2 surfaces. The residual cerium (Ce) ion concentrations remaining on the SiO2 film surface after cleaning, were 0.06, 0.41, and 0.10 ppb for H2 GDW, CO2 GDW, and O2 GDW, respectively.

A DFT study of structural, electrical, and thermodynamical properties of Sc, Ti, V doped ring Beryllium Oxide (BeO)n (n = 3–5) nanoclusters

The geometric, electronic, and thermodynamic properties of ring-shaped beryllium oxide (BeO)n (n = 3–5) clusters along with changes in these characteristics after doping with transition metals—scandium, titanium, and vanadium—have been investigated based on density functional theory (B3LYP/LanL2DZ) calculations. It has been observed that ring-shaped (BeO)n (n = 3–5) clusters have become more stable and effective due to doping with transition metals, and this stability is better for the bigger clusters. In addition, from the IR spectroscopy analysis, it has been observed that both the pristine and doped clusters are formed naturally as there is no imaginary frequency found in these clusters. From the analysis of electronic properties, it has been found that the bandgap drastically reduces after doping in these clusters and shows semiconductivity. Therefore, these doped clusters could be suitable for different high-tech applications in the semiconductor industries.

Probing single electrons across 300-mm spin qubit wafers

Building a fault-tolerant quantum computer will require vast numbers of physical qubits. For qubit technologies based on solid-state electronic devices1–3, integrating millions of qubits in a single processor will require device fabrication to reach a scale comparable to that of the modern complementary metal–oxide–semiconductor (CMOS) industry. Equally important, the scale of cryogenic device testing must keep pace to enable efficient device screening and to improve statistical metrics such as qubit yield and voltage variation. Spin qubits1,4,5 based on electrons in Si have shown impressive control fidelities6–9 but have historically been challenged by yield and process variation10–12. Here we present a testing process using a cryogenic 300-mm wafer prober13 to collect high-volume data on the performance of hundreds of industry-manufactured spin qubit devices at 1.6 K. This testing method provides fast feedback to enable optimization of the CMOS-compatible fabrication process, leading to high yield and low process variation. Using this system, we automate measurements of the operating point of spin qubits and investigate the transitions of single electrons across full wafers. We analyse the random variation in single-electron operating voltages and find that the optimized fabrication process leads to low levels of disorder at the 300-mm scale. Together, these results demonstrate the advances that can be achieved through the application of CMOS-industry techniques to the fabrication and measurement of spin qubit devices.

Thirsty Fabs

How the surprising water footprint of semiconductor manufacturing, datacenters, and AI solutions is sparking sustainable development.

Curvilinear Standard Cell Design for Semiconductor Manufacturing

Curvilinear Standard Cell Design for Semiconductor Manufacturing