Chemical Mechanical Planarization Process Research Articles

Investigation of the impact of environmental conditions on the mechanical properties of thin-film copper wafers

Thin-film copper offers excellent film texture for multilevel interconnections in integrated circuit fabrication due to its superior resistance to electromigration and high electrical conductivity. To perform a chemical mechanical planarization process during semiconductor fabrication of copper, it is necessary to have a thorough understanding of the nanomechanical properties of thin-film copper. In this study, thin-film copper and reacted passivation layers on silicon substrate wafers are investigated for their nanomechanical properties under various environmental conditions. The results of this study indicate that thin-film copper passivation layers have different properties in deionized (DI) water and polishing slurry environments compared to thin-film copper exposed to ambient air. Interestingly, variations in temperature within wet environments do not significantly affect the properties of thin-film copper wafers; but changes in properties are largely driven by chemical processes. The insights gained from this study emphasize the significance of considering both the passivation layers and wet environments in semiconductor fabrication processes, which contributes to the advancement of copper-based interconnect materials and optimization of the chemical mechanical planarization process in semiconductor manufacturing.

Towards Digital Twin Standards in Chemical Mechanical Planarization (CMP) for Advanced Packaging

A digital twin is an integrated multi-physics, multiscale, probabilistic simulation of a system that uses the best available physical models, sensor updates, process history, etc., to mimic and to predict the performance of the system. In the semiconductor industry, there is a need to establish such fundamental, mechanism-based, correlations between process conditions and observed process performance. Such understanding can lead to the creation of industry-wide technical standards, and digital twins (i.e., real time digital replicas of unit processes), that will enable inter-operability, ease process selection and integration, as well as help reduced cost of ownership.In this paper, we revisit our decades old, but still relevant, quest for appropriate mechanism-based metrology to support the digitization of Chemical-Mechanical Polishing (CMP) for the back-end-of line (BEOL), with advanced packaging, specifically hybrid bonding for 3D interconnects, in mind. Here, we consider the digitization of the nanoscale tribological aspects of CMP. For example, many CMP processes use chemically unstable polyurethane pads in a variety of slurry formulations which make the process difficult to understand and to model. We had previously described how the chemical instability of the PU-pads affects the CMP process performance. Here we contextualize the historical performance data, identify data gaps, and propose some next steps towards digital twin CMP modules.

Decoding CMP Challenges: A Metrospection Insight

We live in a digitally connected world with Global data to exceed 180 ZB by 2025 per some estimates1. Memory is the fiber connecting our world. New applications drive continued demand growth for memory and storage applications. Micron is a leader in the memory and storage industry, helping the world use information to enrich lives. As memory devices scale and new memory technologies arise, the requirements for Chemical Mechanical Planarization (CMP) processes are becoming tighter and more stringent node over node. Furthermore, memory scaling continues to become more dependent upon CMP, with not only a steady increase in the number of CMP steps but that of unique CMP steps as well node over node. In this presentation we focus on CMP challenges from a METROSPECTION (Metrology & Inspection) lens. We explore the critical role of inspection and metrology techniques in optimizing CMP for semiconductor memory fabrication. The ability and need for the integration of cutting-edge inspection techniques and metrology strategies emerges as a pivotal element in enhancing CMP cycles of learning and hence the reliability, yield, and overall quality of semiconductor memory devices.1 - Statista

Bridging Technology and Environment: Sustainable Approaches in Semiconductor Chemical Mechanical Planarization (CMP)

As semiconductor technologies continue to advance in areas such as autonomous driving, artificial intelligence, 5G communications, the Internet of Things, and large-scale data processing, the demand for a reliable semiconductor industry is on the rise.(1) This growing demand necessitates the use of advanced device architectures, which heavily rely on two crucial processes: Chemical Mechanical Planarization (CMP) and post-CMP cleaning.(2) This talk will focus on the development of CMP slurries and post-CMP cleaning solutions, emphasizing their vital roles in these processes. We will delve into the comprehensive development of CMP slurries and post-CMP cleaning solutions, highlighting how synthesis methods can significantly affect the surface chemistry of ceria abrasives and impact the removal rates of SiO2 surfaces during STI CMP.(3, 4) Our research also demonstrates how our cleaning solutions can effectively remove even the most 10 nm-sized ceria particles from SiO2 surfaces,(5) thereby enhancing process efficiency. Furthermore, we will discuss the use of aliphatic amino acids as environmentally friendly corrosion inhibitors in CMP slurries, offering a sustainable alternative to the traditionally used benzotriazole (BTA) and addressing key environmental challenges.(6) In the context of the rapidly expanding semiconductor industry, it becomes critical to address global sustainability concerns and environmental health and safety (EHS) objectives. Our research proposes a comprehensive methodology for evaluating the sustainability of CMP consumables in semiconductor manufacturing, with a primary focus on CMP slurries due to their significant market share and shorter usage lifetimes. This research serves as a foundation for future CMP sustainability analyses, encouraging self-correction strategies within the CMP community to reduce environmental impact. We will also introduce a robust framework for the Life Cycle Assessment (LCA) of CMP consumables, marking a crucial step toward achieving these sustainability goals. In summary, this talk will explore the intricate details of CMP and post-CMP processes, their pivotal roles in the advancement of semiconductor manufacturing, and the imperative task of aligning these processes with environmental and sustainability objectives.This talk is dedicated to the special session in honor of Prof. Babu. S. Babu, Advances in Chemical Mechanical Planarization (CMP), Woodhead Publishing (2021).J. Seo, Journal of Materials Research, 36, 235 (2021).J. Seo, J. W. Lee, J. Moon, W. Sigmund and U. Paik, ACS applied materials & interfaces, 6, 7388 (2014).J. Seo, J. H. Kim, M. Lee, J. Moon, D. K. Yi and U. Paik, Journal of colloid and interface science, 483, 177 (2016).J. Seo, A. Gowda and S. Babu, ECS Journal of Solid State Science and Technology, 7, P243 (2018).H. T. T. Tran, J. Gichovi, E. J. Podlaha and J. Seo, in Electrochemical Society Meeting s 243, p. 1809 (2023).

Investigating Slurry Flow Patterns in Chemical Mechanical Polishing (CMP) Using Computational Fluid Dynamic (CFD) Simulations

Chemical Mechanical Polishing has emerged as a leading area approach for planarizing wafer surfaces during semiconductor manufacturing due to its ability to provide defect-free and uniform surfaces. Achieving nanoscale planarity on a wafer and die is one of the most significant steps in the process [1]. As part of the CMP process, it is essential to understand the interactions between consumables to facilitate the production of smooth and mirror-like surfaces [2], [3]. Simulation and numerical modeling have become critical means for elucidating intricate flow patterns to probe the underlying fundamentals of CMP processes. The computational fluid dynamics (CFD) simulations provide a detailed understanding of fluid flow behavior for various conditions [4], [5]. This study uses a three-dimensional CFD simulation to provide an understanding of the slurry flow pattern and its effects on CMP performance. Several CFD simulations were performed to study the effect of different parameters on slurry flow patterns. The simulations provided information on the interaction between the slurry, the wafer surface, and the polishing pad, in addition to the abrasive distribution, slurry velocity, and pressure variations within the CMP tool. It was observed that changing the pad and wafer rotation speeds, the wafer-pad gap size and the center-to-center distance affect the slurry flow conditions. For the same rotational speeds, as the center-to-center distance decreases, the flow velocity increases toward the wafer edge. Moreover, with the same rotation speed, the pad rotation dominates the wafer rotation speed.[1] B. Suryadevara, Advances in Chemical Mechanical Planarization (CMP). Woodhead Publishing, 2021.[2] J. Seo, Journal of Materials Research, 36, 235–257, 2021.[3] A. R. Mazaheri and G. Ahmadi, J Electrochem Soc, 150, G233, 2003.[4] J. Tu, K. Inthavong, and G. Ahmadi, Springer Science & Business Media, 2012.[5] G. Ahmadi, “Chapter 2 -, R. Kohli and K. L. Mittal, Eds., Oxford: William Andrew Publishing, 2012, pp. 81–105.

(Invited) Chemical Mechanical Planarization Integrated Data Collaboration: Uncover Hidden Interactions for Predictive Maintenance and PCN

This article introduces an innovative analytical methodology that employs machine learning to optimize and predict consumable impacts for Chemical Mechanical Planarization (CMP). By harnessing fleet CMP equipment performance and individual equipment characteristics, our proposed approach aims to elevate wafer-level quality, maximize equipment utilization rates, and minimize unnecessary downtime. CMP, a multifaceted process, its performance is often influenced by strong interactions from its critical components including slurry, pad, machine recipe, and dresser usage. This requires control beyond hardware advancements, especially when equipment is pushed to its limits. Therefore, the demand for higher precision on-wafer performance mandates a data collaboration with sophisticated analytical strategy. In addition, a digitalized product change notification (PCN) concept is also introduced to demonstrate effectiveness of applying integrated data analytics collaboratively across semiconductor chain to accelerate material manufacturing process changes, minimizing impacts on wafer results and shortening the learning cycle and ramp time to full production.Our study leverages data analytics to explore differences between equipment within the same fleet of similar processing. Engineers can deploy a process based on the historical performance of specific equipment, supported by a model trained on data from the same fleet. Furthermore, our approach enables each equipment to predict its own fingerprinted consumable maintenance, accurately forecasting the maintenance time for consumable changes. This concept is equally applicable for consumable PCN migrations.The first use case is to demonstrate the use of integrated data for fleet matching of CMP equipment and/or consumable batches running similar processes. This is particularly crucial as the demand for process precision increases and the need to reduce post-CMP result variability in removal rates and wafer-level uniformity intensifies. This approach could add proactive data driven strategy for process engineers who traditionally concentrate on the removal rate, a highly influential factor, with the standard deviation of platen and machine data identified as a key parameter.The second use case employs heatmaps analysis and proposes scatter plots as an intuitive method to comprehend intricate connections between average removal rates and various parameters. Machine learning is particularly advantageous in this context, as no single parameter plays a dominant role. Preliminary observations suggested, for example that dresser and pad characteristics may have compounding impacts to the wafer level performance.In summary, this article provides a roadmap for in-depth CMP data analysis fed into machine learning, aiming to unravel trends and influential factors that can enhance process optimization and shorten the PCN qualification timeline to meet the increasing demands for CMP process precision and efficiency in High Volume Manufacturing (HVM).

Chemical mechanical planarization of nanotwinned copper/polyimide for low temperature hybrid bonding

In this study, fully cured low-temperature polyimide (PI) films and 〈111〉, 〈110〉, 〈200〉, and 〈211〉-oriented Cu films on Si wafers were chosen to investigate the grinding effect on Cu/PI pattern wafers. PI is chemically inert and low cost with good mechanical properties, which is a promising material for hybrid bonding. However, it is challenging to remove PI during chemical mechanical planarization (CMP) process. The removal rate of PI is dominated by the abrasive grinding mechanism. By adjusting the mechanical parameters (abrasive type, size, dose, and platen velocity), we explored the desorption/adsorption (Kpad) and repulsion (Krep) coefficients using a modified Langmuir adsorption model. Such a tool enables us to predict and discover the optimal PI grinding interval. Additionally, results showed that the hardness of Cu with different orientations varied and was inversely proportional to the removal rate. It was found that the 〈111〉-oriented nanotwinned Cu (NT-Cu) possessed high surface hardness and low polishing rate, which benefited the CMP process of Cu/PI hybrid joints. The optimal height profile of NT-Cu/PI was adjusted by abrasives and pad types. PI recess was achieved while Cu was protected from further dishing under harsh grinding environment. As characterized by atomic force microscopy, Cu protrusion of 200 nm was obtained, achieving excellent bonding interfaces for the NT-Cu/PI hybrid bonding under a thermal compression bonding (TCB) process at 220 ℃ for 1 h with 8.3 MPa.

Examining amino acids as environmentally friendly corrosion inhibitors for Cu and Co chemical mechanical planarization

Aliphatic amino acids (AAs) were investigated as environmentally friendly alternatives to benzotriazole (BTA) in the chemical mechanical planarization (CMP) process for Cu interconnects. The study evaluated the corrosion resistance, galvanic corrosion, and polishing removal rates (RR) for Cu and Co in a silica-based slurry containing methionine, glutamic acid, and leucine. Corrosion characteristics were determined using linear sweep voltammetry, and the removal rate was assessed by polishing wafers under controlled CMP conditions. Metal surfaces were analyzed by X-ray photoelectron spectroscopy, and scanning electron microscopy. Results indicated that the distinctive functional groups in the amino acids influenced corrosion and polishing behaviors driven by different Cu species in the passivating film layer. Methionine with its sulfur-containing side chain, exhibited exceptional inhibition capabilities by stabilizing Cu(Ⅰ) species compared to Cu(Ⅱ), involving a combination of physisorption and chemisorption. It also effectively controlled Cu-Co galvanic corrosion with a low igc = 0.9 μA/cm². Leucine, with its branched hydrocarbon chain, mitigated Cu corrosion through physisorption (20.5 kJ/mol), but increased galvanic corrosion for Co (igc = 45 μA/cm²). Leucine formed a thin passive layer that easily broke down during the Cu(I) to Cu(II) transition. Glutamic acid, with its additional carboxylic acid group, was not effective as a corrosion inhibitor but showed a relatively low |∆Ecorr| = 5 mV. Metal removal rates of Cu and Co followed the same trend with corrosion results. The selectivity of Cu:Co:TEOS removal with methionine was more comparable than that achieved with other additives, beneficial for Cu barrier CMP.

Synergetic Effect of Chemical Components in a Tungsten CMP Slurry and the Evolution of Tungsten Surface Species

A novel slurry containing 50 ppm Fe3+, 2 wt% H2O2, 75 ppm oxalic acid, and abrasive nano-silica particles were utilized in a W chemical mechanical planarization process. Using this slurry, a W material removal rate of 710 Å min−1 and surface roughness of 1 nm were achieved. According to X-ray photoelectron spectroscopy data, surface W was first transformed to WO2 via its dissolution in H2O2, and then Fe3+ ions serving as a catalyst promoted the conversion of tungsten to WO3 through the WO2 intermediate in the presence of H2O2. Oxalic acid was added to coordinate with H2O2 and thus improve the slurry stability through a “wrapping” mechanism without decreasing the W material removal rate. Scanning electron microscopy and atomic force microscopy observations revealed that the W surface morphology changed first from coarse elongated grains to a discontinuous structure after the uneven oxidation by H2O2 and then to a wrinkled soft surface by Fe3+ and H2O2. Finally, it was ground with abrasive nano-silica particles to achieve a high degree of flatness.

Efficient nanoscale characterization of wafer surfaces using intelligent sampling and Bayesian optimization

The geometrical attributes of a wafer, such as thickness, uniformity, and local curvature, serve as eminent determinants of its quality. Therefore, measurements that are both rapid and accurate are paramount as multi-stage fabrication processes hinge on them to ensure high product reliability, proper process control, and maximum efficiency. The manual wafer profiling measurement approach in use today is, unfortunately, a time-consuming process. This creates a need for fast and prompt analysis of wafer attributes. This research thus proposes an iterative sampling method that boasts reducing the number of samplings down to a minimum without the need to discard a sufficient level of accuracy for the purpose of reliable attribute estimation. Subsequently, we suggest a series of methods to improve the monitoring and evaluation of the surface topography, all within the context of ultra-precision/nanoscale manufacturing processes, such as ultra-precision machining (UPM) and chemical–mechanical planarization (CMP). For the purpose of both real-time and exception-based detection of anomalies in the UPM process, we constructed a process-machine interaction (PMI) model with Bayesian learning, which amalgamates the received data across heterogeneous sensors such as force, vibration, and acoustic emission in situ. Similarly, the CMP process is quantitatively characterized by a multi-scaled multilayer nonlinear decomposition model with predictive capabilities. This model helps understand the physio-mechanical phenomena underlying both CMP and the UPM processes and describe and predict the nonlinear interactions, as evidenced by the experimentally collected vibration signal patterns.

64‐1: Protrusion‐free LTPS Using the CMP Process and Its OLED Application

Low Temperature Polysilicon (LTPS) is the most popular channel material used in the thin film transistor and it has the protrusions due to the volume expansion of Si and the presence of its native oxide. The protrusions cause the negative effects such as interface trap, hysteresis and so on. In that sense, the heights need to be lowered or eventually flattened.In this study, we used the chemical mechanical planarization (CMP) technique to reduce the surface roughness of LTPS to the 1nm and even lower. One of the most difficult problmes in the large sized CMP process is the removal of slurry residues after CMP and we successfully overcome the issue by using the hydrophilic angent.Finally, we are succesfully able to prepare AMOLED panel with reduced gate insulator thickness by using the CMP process and it shows less hysteresis results.

Particle size dependence of nanoclustered ceria abrasives on surface activity and chemical mechanical planarization performance

The particle size of cerium oxide (ceria) abrasives plays a crucial role in the design and development of advanced self-stopping slurries for the chemical mechanical planarization (CMP) process in semiconductor device manufacturing. However, a lack of ceria nanoparticles (NPs) with a narrow size distribution and uniform shape poses challenges in investigating the fundamental principles of the relationship between abrasive size and CMP performance. In this study, we thoroughly investigated the effect of ceria NP size on the removal rate (RR) of silicon oxide using uniform-sized nanoclustered (NC) ceria NPs with three diameters: 51.4 nm (NC51), 68.6 nm (NC69), and 108.0 nm (NC108). While all three types of ceria NPs shared a crystallinity of ∼ 70.6 %, they exhibited distinct physicochemical characteristics. As the particle size decreased, the specific surface area and chemical activity (i.e., Ce3+/Ce4+) increased by 155 % and 69 %, respectively. In the CMP process, where direct contact between the abrasives and wafer occurs, the abrasive size influences CMP performance. Experimentally, NC51 achieved the highest RR among the three slurries, along with complex size-dependent contact properties and chemical activities. These experimental results offer significant insights into precise semiconductor manufacturing in a practical manner.

Experimental and Density Functional Theory Study of Inhibitors on Cobalt Corrosion for Chemical Mechanical Planarization Process

In the localized and global chemical mechanical planarization (CMP) process of Co barrier layers, corrosion inhibitors play a crucial role in the removal rate (RR) of Co and the post-polishing surface quality. In this study, quantum chemical calculations were employed to investigate the molecular reactivity of benzotriazole (BTA), 5-methyl benzotriazole (TTA), and 2,2’-{[(methyl-1H-benzotriazol-1-yl)methyl]imino}bis-ethanol (TT-LYK), and their inhibitory performance was predicted to follow the order of TT-LYK > TTA > BTA. The experimental results validated the accuracy of the predicted order under static conditions. However, the performance order of the inhibitors shifted to TTA > BTA > TT-LYK due to varying degrees of damage caused by mechanical friction to the inhibitory film. Nevertheless, all three inhibitors are capable of effectively inhibiting the Co RR to meet industrial requirements. Ultimately, considering the outstanding inhibitory performance of TT-LYK and its ease of removal during post-cleaning processes, TT-LYK is determined as the most promising inhibitor for Co barrier layer CMP.

Release of Redox Additives Via Megasonic Energy for High Selectivity/Low-Stress STI CMP

The advancements in a wide array of semiconductor manufacturing processing have been pivotal for the extension of Moore’s law the Chemical Mechanical Planarization (CMP) process has become an area of focus due to the stringent surface topography requirements. CMP utilizes complex slurry formulations containing abrasive nanoparticles and additives to reduce substrate defectivity and achieve angstrom-level uniformity. One area that has gained significant attention is Shallow Trench Isolation (STI), where the modulation and control of the surface oxidation state of the ceria, specifically the Ce3+ state, is critical to TEOS removal. In the Ce3+ state, defect states (i.e., oxygen vacancies (Ovacs)) on the surface are highly electron deficient (i.e., electrophilic) and allow the nucleophilic substrate to complex. By modulating the electrophilic properties of the particle, the kinetics of surface complexations required for effective TEOS removal can be controlled. This work focuses on developing stimuli-responsive capsules that can be added to STI slurry formulations to promote the controlled release of redox-active additives that can be used to drive the oxidation state of the CeO2 nanoparticle (Ce3+/Ce4+). By encapsulating the additive in a polymeric system, the reactive properties of the slurry are limited until the additive is released. In this work, megasonic action was employed to disrupt the capsules releasing the additives on demand to facilitate the modulation of the CeO2 surface redox states. Results have shown that the addition of a controlled-release capsule with an additive resulted in the increase of Ovacs at the CeO2 surface. This led to an increase from 1800 Å/min to 3450 Å/min using a constant 2.0 W/cm2 continuous flow megasonic flux. Furthermore, the overall defectivity decreased significantly with respect to both particle contamination and surface scratching. Lastly, the nature of the polymeric capsule has been shown to enhance selectivity between TEOS and silicon nitride and preliminarily appears to be megasonic flux dependent.

Survey of Activated Cleaning Chemistries for Low-Stress Post-Chemical Mechanical Planarization Cleaning of Silicon Carbide

Integrated Circuit (IC) technologies have begun transitioning to wide band gap (WBG) substrates (i.e., Silicon carbide (SiC)) due to the intrinsic properties (i.e., high capacitance, thermal stability, and wear resistance) of these semiconducting materials. This has led to the development of Chemical Mechanical Planarization (CMP) processes that use aggressive chemical conditions and increasing mechanical forces to reach optimal material removal rates (MRR). The harsh CMP process parameters often result in contamination/defectivity (i.e., organic residue, abrasive particles, etc.) left on post-polish substrate surfaces and thus posing difficulty within the post-CMP cleaning process. Traditionally, post-CMP cleaning utilizes a polyvinyl alcohol (PVA) brush scrubbing method on the substrate surface. This has been shown to cause secondary defects (i.e., scratches, particle agglomerations, etc.) that inevitably lead to overall device failure. Alternative post-CMP methods utilize non-contact cleaning via megasonic energy to target removing defects (i.e., organic residue, pad debris, particle agglomerations, etc.) and avoid secondary defect generation. This research surveys post-CMP cleaning processes (contact and non-contact) on SiC substrates with various cleaning chemistries (i.e., supramolecular surfactants, catalytic complexes for generating reactive oxygen species (ROS), non-metal catalyst). Initial results show that supramolecular surfactants can produce high particle removal efficiency (PRE) due to their ability to be broken down into their monomeric units and encapsulate any removed residue under both brush and megasonic conditions Additionally, increased ROS generation disrupts the non-covalently adsorbed defects to the SiC surface and leads to an increased cleaning efficiency while decreasing the overall variability of particle removal results. Combining the ROS systems' disruptive power with surfactants' encapsulation mode, a highly efficient clean with low variability can be achieved under low-stress conditions.

Development of a Lab-Scale Tribo-Electrochemical Approach for Chemical Mechanical Planarization

During chemical mechanical planarization (CMP), the electrochemical interactions between slurry additives and the polishing substrate metal films favor the formation of easily removable species that are then detached by mechanical abrasion effects of the CMP pad and nanoparticle abrasives in slurry. Yet, neither the mechanical nor chemical aspects alone dictate the CMP process; rather, it is the intertwined dependencies of tribology and electrochemistry that often control the polish performance. This highlights the importance of the functional synergies, otherwise known as tribo-electrochemistry, which has been largely under-explored in the CMP research community.This work discusses the development of a novel small-scale tribo-electrochemical approach for metal CMP. It covers the tool design aspects, tribo-electrochemical measurements such as open circuit potentiometry (OCP) and polarization resistance, and relevance to CMP applications. The system is developed based on a small-scale CMP polisher coupled to a 3-electrode cell design circuit that is connected to a potentiostat. The design utilizes a common type of CMP metal film as the working electrode, with the accompanying pad and slurry consumables serving as baseline for method development. All other process conditions, including rotating speed, downforce, polish-hold duration, and pad conditioning process follow typical CMP standards.The results show tribo-electrochemical responses to the presence of catalyst (C), oxidizer (O), inhibitor (I), and abrasive (A) in the experimental slurries. The data is of significant value in terms of the anodic & cathodic site functions on the CMP metal surface and in the context of material removal. In addition, notable sensitivity to pad texture, downforce, and temperature effects were observed, and removal rate measurements correlated with polarization resistance for varied levels of the O and I additives. In summary, the tribo-electrochemical aspects of CMP have been probed and an innovative analytical approach has been developed and demonstrated.

Oxidation differences on Si- versus C-terminated surfaces of SiC during planarization in the fabrication of high-power, high-frequency semiconductor device

Silicon carbide (SiC) wafers have attracted attention as a material for advanced power semiconductor device applications due to their high bandgap and stability at high temperatures and voltages. However, the inherent chemical and mechanical stability of SiC poses significant challenges in the chemical mechanical planarization (CMP) process, an essential step in reducing defects and improving surface flatness. SiC exhibits different mechanical and chemical properties depending on SiC terminal faces, affecting SiC oxidation behavior during the CMP process. Here, we investigate the process of oxide layer formation during the CMP process and how it relates to the SiC terminal faces. The results show that under the same conditions, the C-terminated face (C-face) exhibits higher oxidation reaction kinetics than the Si-terminated face (Si-face), forming an oxide layer of finer particles. Due to the different oxidation kinetic tendencies, the oxide layer formed on the C-face has a higher friction coefficient and more defects than the oxide layer formed on the Si-face. This results in a higher removal rate during CMP for the C-face than the Si-face. Furthermore, by controlling the physicochemical properties of the oxide film, high removal rates can be achieved by friction with the pad alone, without the need for nanoparticle abrasives.

Restricted hydrolysis reaction of Si3N4 via nonionic polymer adsorption in advanced shallow trench isolation chemical mechanical planarization

The adoption of ultra-fine nanoparticles as an abrasive has garnered notable attention in shallow trench isolation (STI) chemical mechanical planarization (CMP) due to its potential in scratch reduction. However, the poor removal rate, attributed to the physicochemical interaction between abrasive and conventional organic passivation agent, limits the introduction of ultra-fine nanoparticles. Here, a nonionic polymer polyvinyl alcohol (PVA), without particle interaction, is proposed as a novel passivation agent to restrict the hydrolysis reaction of Si3N4 film in the advanced STI CMP process. The selective adsorption of PVA onto the Si3N4 film lowers its removal rate by suppressing the hydrolysis reaction via hydrogen bonding while the removal rate of SiO2 film remains constant. Moreover, the uniformity of SiO2 removal is improved due to the enhanced fluidity of slurry, resulting from increased wettability between pad and slurry. As a result, the removal selectivity between both films has been improved to 113.4:1 and the in-wafer-uniformity (IWU) of SiO2 film is supplemented by 96.8 % simultaneously.

A collocation grids method for determining pad conditioning coverage and density under long time conditioning process

In the Chemical Mechanical Planarization (CMP) process for semiconductor wafer fabrication, the polishing pads need to be continuously conditioned to ensure appropriate surface characteristics, which causes wear to the pads and subsequently affects the quality of CMP. Tuning the conditioning parameters is typically determined through expensive experimental trials. An effective pad conditioning simulation platform to model the conditioning process can significantly reduce the time and cost of the process development cycle. However, to mimic the real-life pad conditioning process, the simulation needs to track hundreds or even thousands of micro-sized diamond tips to check the contact points on the pads continuously during the conditioning process that spans many hours. This makes simulating pad conditioning over the entire pad for the duration of the process very challenging. In this work, we propose a low-cost numerical approach for simulating the pad conditioning process based on a collocation grid method and parametric mapping. Important numerical aspects such as time step size characterization, jump-over ratio, simulation efficiency, and error control are systematically analysed and discussed. Several benchmark examples are performed to evaluate the effectiveness of the proposed method.

Design of “Soft” Cleaning Processes for Emerging Substrates via Stimuli Responsive Chemistry

The Chemical Mechanical Planarization (CMP) process (polishing and substrate cleaning) results in defects that can be classified as mechanical (i.e., scratching), chemical (i.e., corrosion), or physiochemical (i.e., adsorbed contaminants) according to the mechanism of formation. This work will focus on the rationale design of p-CMP cleaning systems for emerging materials (silicon carbide (SiC)) that activate the cleaning chemistry via external stimuli such as megasonic energy. More specifically, using megasonic energy in the presence of supramolecular assemblies such as micelles and vesicles was employed for a “soft” (low shear force) defect removal process. Results indicate a correlation between the structure of the “soft” cleaning additives and induced megasonic energy on overall simulated defect removal. It was determined that effective particle removal was a second-order kinetic process with a concentration dependency (i.e. above and below the critical micelle concentration (CMC)) emerging as a key driver for the defect removal rate. Although, one apparent drawback is the generation of post-cleaning carbon residue due to the adsorption of the supramolecular structures to the SiC substrate.