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This study explores the effects of atomic-level factors on the performance of CeO 2 when interacting with SiO 2 films. It specifically examines how different precursors, the ratio of Ce to OH, and reaction temperatures influence outcomes. Our findings reveal that smaller particles, around 5 nm in size, created using a Ce4+ precursor, are more effective at removing SiO 2 films compared to larger, more crystalline particles from a Ce3+ precursor. This is due to their increased interaction with the SiO 2 film during the polishing process, challenging the conventional emphasis on mechanical abrasion in chemical mechanical planarization (CMP) and highlighting the crucial role of chemical interactions. Further analysis through FT-IR and TGA-FTIR techniques showed distinct functional group profiles on the ceria surfaces. Ceria derived from Ce4+ exhibited a higher presence of OH and NO 3 groups, enhancing adsorption capabilities, as verified by CHN analysis. Importantly, first-principles calculations identified these surface groups as key to improving adhesion to SiO 2 films. Surfaces of amorphous CeO 2 , rich in -OH and -NO 3 groups, showed significantly higher adhesion levels than their crystalline counterparts, connecting crystallinity to chemical functionality. This new insight leads to the possibility of designing next-generation CeO 2 abrasives with increased chemical activity. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • High-dimensional modeling approach predicts material removal rate (MRR) based on polishing pad surface topography. • The proposed approach reduces errors by 53-70% compared to MRR prediction using single roughness parameters. • Taking into account the entire pad topography yielded a 17% improvement in prediction error compared to solely considering pad asperity group. • Integrating the entire pad surface functionality addresses unexpected nonlinear variations during the process. [ABSTRACT FROM AUTHOR]

Chemical Mechanical Planarization (CMP) technology in integrated circuit manufacturing plays a critical role in realizing local and global flatness of silicon wafer surface. There are still some problems to be solved in industrial production, such as long period of CMP process debugging and various polishing materials, which lead to a more complicated Material Removal Rate (MRR) calculation during the CMP process. Based on the 2016 PHM Challenge dataset, this paper proposes a prediction method based on improved genetic algorithm to optimize BP neural network, which can be used to predict MRR more accurately. It is more difficult to predict MRR because of the many evaluation metrics involved in the CMP process. In this paper, grey relation analysis and principal component analysis methods are introduced to extract and reduce the dimension of 19 evaluation indicators. After reconstruction, six principal components are obtained for input to the BP neural network. The improved adaptive genetic algorithm is used to optimize the weight threshold of BP neural network, and the prediction results are compared with the same type of algorithms. The experimental results show that the BP neural network improved by feature selection and adaptive genetic algorithm has the smallest error with the real results and the highest prediction accuracy for MRR.

Chemical mechanical planarization (CMP) is a complex and high-accuracy polishing process that creates a smooth and planar material surface. One of the key challenges of CMP is to predict the material removal rate (MRR) accurately. With the development of artificial intelligence techniques, numerous data-driven models have been developed to predict the MRR in the CMP process. However, these methods are not capable of considering surface topography in MRR predictions because it is difficult to observe and measure the surface topography. To address this issue, we propose a graphical model and a conditional variational autoencoder to extract the features of surface topography in the CMP process. Moreover, process variables and the extracted features of surface topography are fed into an ensemble learning-based predictive model to predict the MRR. Experimental results have shown that the proposed method can predict the MRR accurately with a root mean squared error of 6.12 nm/min, and it outperforms physics-based machine learning and data-driven methods. [ABSTRACT FROM AUTHOR]

To reduce the manufacturing cost of integrated circuits, the size of the silicon wafer has become larger, with the diameter increasing from 200 to 450 mm. The large diameter silicon wafer poses challenges to the manufacturing process, as its surface profile is more difficult to control and the planarization process is harder to optimize in mass production. Based on different types of contact, gap adjustment and convex pad dressing methods were proposed in this paper to control the pressure distribution on the wafer, which further influenced the wafer profile. Pressure distributions with different gap values and different pad profiles were explained with contact mechanics and verified by simulations. The simulation results show that a negative gap value and a convex pad profile contribute to the improvement of the pressure uniformity on the wafer. Both methods were then applied in the double-sided planarization (DSP) process of 300 mm silicon wafers. The results from the test run of the DSP process show that wafer flatness is improved with a negative gap value. This indicates that gap adjustment is an effective approach for wafer profile control. In the subsequent mass production of the DSP process, silicon wafers with a global flatness of 120 nm and a site flatness of 21.7 nm were obtained. [ABSTRACT FROM AUTHOR]

With the reduction of technology nodes to less than 10 nm, ruthenium (Ru) has emerged as a viable replacement for copper (Cu) for back-end-of-the-line (BEOL) interconnects. Good surface uniformity while maintaining an appropriate thickness of Ru is required during the fabrication process, which can be achieved by employing the chemical mechanical planarization (CMP) process. However, a crucial step in the CMP process is to attain reasonable removal rates of high-mechanical-strength and chemically inert metals such as Ru. Hence, this study investigates a competent CMP slurry comprising potassium iodate (KIO3) as an oxidizer and fumed silica as an abrasive for polishing Ru. The polishing results show that adding KIO3 to fumed silica modifies the silica particles, thereby enhancing the removal rates of the metal. thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR) and ultraviolet-visible spectroscopy (UV-Vis) characterization confirm the modification of the silica particles by KIO3 as an oxidizer. The studies reveal that the abrasive concentration usage was reduced to 60% due to silica modification. The surface morphology of the treated metal surface was evaluated using field emission scanning electron microscopy (FESEM). No adsorbed contamination or pitting is observed on the metal surface. The inhibition effect of 1,2,3-benzotriazole (BTA) was investigated in detail by performing potentiodynamic polarization experiments. The nature of the metal dissolution in KIO3 was investigated by examining the effect of solution temperature on the etch rates. The thermodynamic process of the system was found to be endothermic in nature, and the dissolution followed an associative mechanism. The results reported in this paper reveal that the proposed slurry can be used to significantly improve Ru polishing efficiency and provide desired selectivity without compromising the surface quality of the metal. [ABSTRACT FROM AUTHOR]

With the advent of the 5G information age, the production and processing of computer chips have attracted people's attention. In order to adapt to the rapid development in the field of chip manufacturing, higher requirements are placed on chemical mechanical planarization (CMP). Due to the porous structure of the polishing pad surface, the polishing pad is prone to clogging and its performance gradually decreases during the polishing process. Therefore, the use of diamond disc conditioner to dress the polishing pad in the CMP process is an indispensable part. In this paper, a new method of measuring the conditioner disc is proposed, the wax is melted and covered on the disc, and after a period of time, it solidifies on the surface of the dressing disc to obtain the protrusion height of the grains; this method has more advantages than measuring with an optical microscope because of the error caused by the diffuse reflection of the microscope. For diamond conditioners used in the experiments, the motion trajectory of abrasive grains is simulated, which is the basis on choosing the experimental method, and the material removal rate model of the polishing pad is established. In addition, after polishing the silicon wafer with a polishing pad for 2 h, the diamond conditioners with orderly distributed abrasive grains are used to dress the pad, the discs can remove the blocking material on the surface of the polishing pad after dressing 30 min, and the dressing effect and efficiency of the diamond disc with grain size of 100 μm are more obvious. The surface roughness and the liquid contact angle of the polishing pad before and after dressing are measured; after a period of conditioning, they can be restored to a state similar to that of the new polishing pad. General scheme of the research [ABSTRACT FROM AUTHOR]

The effect of the two-way injection method during a copper chemical mechanical planarization (CMP) process was investigated. The two-way slurry-injection method has the advantage of not only preventing the degradation of the slurry, but also shortening the process time because the mixing process of the slurry and H2O2 is not required. Compared to the conventional method, the two-way injection method has an equivalent level of less than 9% in material removal rate. In particular, when injecting near the center of the pad, the influence according to the positions and flow rates of the two nozzles is also insignificant, so it deserves to be studied as a slurry-supply method. [ABSTRACT FROM AUTHOR]

Ruthenium (Ru) has the advantages of low resistivity, high thermal stability, and good adhesion and wettability, making it an important solution for breaking through the 14 nm process as a new barrier layer material for multilayer copper interconnect. The purpose of copper (Cu) interconnect Ru-based barrier layer chemical mechanical polishing (CMP) is to obtain a highly flat surface, which not only includes the ideal Ru removal rate and selectivity of the Cu/Ru removal rate, but also inhibition of Cu corrosion. In this article, the corrosion inhibition mechanism of environmentally friendly and highly water-soluble inhibitor 5-methylthio-1 H-tetrazole (MTT) on Cu was studied. The inhibitory effect of MTT on Cu was analyzed through static etching rate testing, electrochemical experiments, and surface characterization. A systematic test was conducted on the planarization performance of the optimized polishing slurry. The corrosion inhibition mechanism of MTT on Cu was revealed through quantum chemical calculations and XPS testing. The experimental results show that the corrosion inhibitor MTT can effectively improve the surface quality of Cu. Quantum chemical calculations and experimental results indicate that MTT can be adsorbed on the surface of Cu through both physical and chemical methods, forming a dense passivation film, thereby inhibiting the corrosion of Cu. The optimized ratio of Ru-based barrier layer CMP polishing slurry was 5 wt% SiO 2 , 0.15 wt% H 2 O 2 , 20 mM ethylenediamine (EDA), 5 mM K 4 Fe(CN) 6 , and 2000 ppm MTT, resulted in the Cu/Ru removal rate selectivity of 1:1.18 and the Cu/Ru corrosion potential difference of 3 mV. The average correction values for dishing and erosion on the test pattern wafer of the Ru barrier layer were 355 Å and 280 Å, respectively. The results of this study indicate that MTT is an effective copper corrosion inhibitor in alkaline H 2 O 2 -based polishing slurry, providing meaningful references for Ru-based barrier layer copper interconnect CMP. [Display omitted] [ABSTRACT FROM AUTHOR]

The effect of grain size on the tungsten (W) material removal rate (MRR) was studied during chemical mechanical planarization (CMP) process in this paper. It was found that the average grain size of atomic layer deposition (ALD) W was significantly smaller than that of chemical vapor deposition (CVD) W by TEM and SEM analysis. The MRR of ALD W with small grain size was higher than that of CVD W with large grain size. The static etch rate and XPS results indicated that the W film with smaller grain size had a higher oxidation rate, which were further demonstrated by the analysis of potentiodynamic polarization and the electrochemical impedance spectroscopy (EIS). This can be explained that W film with smaller grain size has a higher grain boundary density, leading to easier oxidizers diffusion along the grain boundaries and faster oxidation in the W film surface consequently. According to the oxidation-removal model, it is concluded that smaller grain size facilitates faster oxidation on the W film surface, leading to higher material removal rates during W CMP process. [ABSTRACT FROM AUTHOR]

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 i gc = 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 (i gc = 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 |∆ E corr | = 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. [Display omitted] • Aliphatic amino acids were studied as eco-friendly corrosion inhibitors for Cu and Co CMP processes. • Methionine, glutamic acid, and leucine were tested for corrosion resistance, galvanic corrosion, and metal removal rates in silica slurry. • Methionine significantly inhibited corrosion due to its sulfur-containing side chain, stabilizing Cu(I) and reducing Cu-Co galvanic corrosion. • Leucine showed limited inhibition, while glutamic acid promoted Cu(II) formation, negatively affecting corrosion and polishing behaviors. [ABSTRACT FROM AUTHOR]

This work reports urea as a promising complexing agent in sodium carbonate-based alumina slurry for chemical–mechanical planarization (CMP) of tantalum and copper. Ta and Cu were polished using Na2CO3 (1 wt. %) with alumina (2 wt. %) in the presence and absence of urea. The effect of slurry pH, urea concentration, applied downward pressure and platen rotational speed were deliberated and the outcomes conveyed. Prior to the addition of urea, the Ta removal rate (RR) was observed to increase with pH from acidic to alkaline, having a maximum RR at pH 11. However, Cu RR decreases with increasing pH with minimum RR at pH 11. With the addition of urea in the slurry, a Cu to Ta removal rate selectivity of nearly 1:1 was encountered at pH 11. The addition of urea simultaneous boosts the Ta RR and suppresses Cu RR at pH 11, as it adsorbs on the metal surface. Potentiodynamic polarization was conducted to determine the corrosion current (Icorr) and the corrosion potential (Ecorr). Electrochemical impedance spectroscopy of both metals was carried out in the proposed formulation and the obtained outcomes are elaborated.

The material removal rate (MRR) plays a critical role in the chemical mechanical planarization (CMP) process in the semiconductor industry. Many physics-based and data-driven approaches have been proposed to-date to predict the MRR. Nevertheless, most of them neglect the underlying equipment structure containing essential interaction mechanisms among different components. To fill the gap, this paper proposes a novel hypergraph convolution network (HGCN) based approach for predicting MRR in the CMP process. The main contributions include: (1) a generic hypergraph model to represent the interrelationships of complex equipment; and (2) a temporal-based prediction approach to learn the complex data correlation and high-order representation based on the hypergraph. To validate the effectiveness of the proposed approach, a case study is conducted by comparing with other cutting-edge models, of which it outperforms in several metrics. It is envisioned that this research can also bring insightful knowledge to similar scenarios in the manufacturing process. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Electrochemistry is the property of Journal of Electrochemistry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The adsorption performance and inhibition mechanism of azoles inhibitors on ruthenium (Ru) at 9 pH, were investigated in this study using electrochemical techniques and density functional theory (DFT) simulations. • BTAH offers higher corrosion inhibition for Ru followed by TAZ and IMD. • Work establishes a framework for designing effective Ru CMP slurries with suitable corrosion inhibitors to control Ru dissolution rate. The adsorption performance and inhibition mechanism of azoles; Imidazole (IMD), 1, 2, 4-Triazole (TAZ), and 1, 2, 3-Benzotriazole (BTAH) inhibitors on ruthenium (Ru) at 9 pH, were investigated in this study using electrochemical techniques and density functional theory (DFT) simulations. The electrochemical data demonstrate that the three azole compounds being investigated are mixed type corrosion inhibitors with anodic predominance, and the order of corrosion inhibition efficiency for Ru is BTAH > TAZ > IMD. The azole compounds suppress the metal dissolution reaction rate by adhering on to the Ru surface and nature of adsorption (combined physisorption and chemisorption) is explained via Langmuir adsorption isotherm model. The increase in contact angle on the Ru surface verified the successful adsorption of the three organic inhibitors on the Ru surface. Furthermore, the suppression of corrosion reaction kinetics is validated with field emission scanning electron microscopy (FESEM) images. The results of quantum chemistry calculations indicate that the corrosion inhibition effect of BTAH is superior to that of IMD and TAZ. Further, using molecular dynamics modelling, the reliable configuration of the organic inhibitor molecules on the Ru (001) surface in aqueous medium was investigated and the total adsorption energy was computed. The Ru metal was effectively protected in an alkaline medium by the BTAH inhibitor. Electrochemical, isotherm, MC simulation, and surface analysis provide confirmation. [Display omitted] [ABSTRACT FROM AUTHOR]

Copper-bearing wastewater from chemical mechanical planarization (CMP) is a typical semiconductor development byproduct. How to effectively treat Cu2+ in the CMP wastewater is a great concern in the microchip manufacturing industry. In this study, we investigated the potential for the microbial removal of Cu2+ by a multiple heavy metal-resistant bacterium Cupriavidus gilardii CR3. The environmental factors, including pH, nano-SiO2, ionic strengths, and initial concentrations of Cu2+, and adsorption times on the bioremoval of Cu2+ in CMP wastewater were optimized. Under optimal condition, the maximum biosorption capacity for Cu2+ was 18.25 mg g−1 and the bioremoval rate was 95.2%. The Freundlich model is described well for the biosorption of Cu2+ in CMP wastewater in the presence of nano-SiO2 (R2 = 0.99). The biosorption process obeyed the pseudo-second-order kinetic equation (R2 > 0.99). In the column experiment, the advection–dispersion–retention model fitted the breakthrough curve of all experiments well (R2 > 0.95). The attachment coefficient in the sand matrix coated by CR3 biofilm was 2.24–2.80 times as that in clean sand. Overall, C. gilardii CR3 is a promising candidate to remove Cu2+ from CMP wastewater. Nano-SiO2 in CMP wastewater did not inhibit the bioremoval of Cu2+ but showed a slight promotion effect instead. HIGHLIGHTS Heavy metal-resistant bacterium Cupriavidus gilardii CR3 can effectively remove Cu2+ from the CMP wastewater in the presence of nano-SiO2.; Nano-SiO2 under the common concentration in CMP wastewater does not affect the bioremoval of Cu2+.; The Freundlich model is better for describing the biosorption of Cu2+ CMP wastewater in the presence of Nano-SiO2 relative to the Langmuir model.;

The effects of dicarboxylic acids (oxalic acid, malonic acid and succinic acid) on the H 2 O 2 stability during tungsten (W) chemical mechanical planarization (CMP) process were studied for the first time. The dicarboxylic acids can coordinate with Fe3+ in the slurry and impact the reaction process by blocking the interaction between H 2 O 2 molecule and Fe3+, decreasing the Fe-complex redox potential and promoting the reduction of Fe3+ to Fe2+. Among the three dicarboxylic acids, oxalic acid shows the superior coordination ability with Fe3+, resulting in a significant improvement of H 2 O 2 stability in the slurry. Appropriate amount of oxalic acid can improve the wafer surface quality (Ra=1.435 nm) while keeping the high material removal rate (MRR = 917 Å/min) at the same time. An increasing amount of dicarboxylic acids can result in a high degree of coordination between Fe3+ and dicarboxylic acid in the slurry. The results also indicate that the main reaction path from Fe3+ to Fe2+ in this system is the reaction between Fe3+ and H 2 O 2. Meanwhile, the MRR keeps almost unchanged after one-week storage with the 2:1 mol ratio of oxalic acid to Fe3+. [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel CMP for low temperature NT-Cu/PI hybrid bonding is reported. • Cured PI recesses whereas NT-Cu protrudes under a harsh grinding environment. • Langmuir adsorption model was modified to predict optimal PI grinding interval. • NT-Cu with high hardness and lowest polishing rate benefits hybrid CMP with PI. • NT-Cu protruding 200 nm enables excellent NT-Cu/PI bonding at 220 ℃ for 1 h. In this study, fully cured low-temperature polyimide (PI) films and 〈1 1 1〉, 〈1 1 0〉, 〈2 0 0〉, and 〈2 1 1〉-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 (K pad) and repulsion (K rep) 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 〈1 1 1〉-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. [ABSTRACT FROM AUTHOR]

Chemical mechanical planarization (CMP), a crucial process in semiconductor manufacturing, raises environmental concerns due to increased waste generation, prompting questions about its long-term sustainability. To address this issue, we explore an innovative approach to the waste management and recycling process by introducing magnetite (Fe 3 O 4) particles coated with SiO 2 layers, aimed at enhancing the CMP recycling efficiency. These core-shell particles exhibit controlled shell thickness, and modified surface charges, as confirmed by various analytical techniques. Our results reveal that slurries containing Fe 3 O 4 @SiO 2 particles significantly improve W removal rates and surface smoothness compared to conventional slurries, attributed to the optimal mechanical properties. Moreover, the magnetite core facilitates the magnetic separation and recovery of used particles from spent slurries, offering a cost-effective and environmentally friendly solution. The recycling efficacy of these particles is demonstrated through multiple CMP cycles, showing consistent removal rates and surface quality, highlighting the potential of magnetic separation in sustainable CMP. [Display omitted] • Fe 3 O 4 @SiO 2 nanoparticles enhance W CMP efficiency and surface quality. • Precisely adjustable SiO 2 shell thickness improves particle functionality. • Magnetic recovery and reuse of particles are demonstrated for sustainability. • Enhanced W removal rate and surface quality with optimized shell thickness. • Findings underscore importance of mechanical properties in polishing efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • Surface characteristics of spherical nanoclustered (NC)-ceria nanoparticles (NPs) with varying diameters were investigated. • NC-ceria NPs exhibited a high specific surface area and surface activity (i.e., Ce3+/Ce4+). • The smallest NC-ceria NP slurry achieved the highest SiO 2 removal rate in the chemical mechanical planarization process. 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. [ABSTRACT FROM AUTHOR]

In the present work, tantalum (Ta) and copper (Cu) chemical mechanical polishing (CMP) has been carried out using a polishing slurry containing 2 wt% alumina (abrasive), 1 wt% potassium carbonate (oxidizer), and 0.25 wt% each urea and citric acid (additives). The Cu removal rate (RR) decreases with an increase in pH and is found to be minimum at pH 11 while Ta RR increases till neutral pH and then decreases till pH 11 having maximum RR at pH 7. Ta to Cu removal rate selectivity of 1:1.02 is attained at pH 11. Other parameters such as the effect of additives concentration, applied pressure, and platen rotational speed are also studied and results are reported to get a clear scenario. Surface charge analysis reveals that with change in pH, zeta potential alters and 30mV zeta potential is found at pH 11 which confirms the stability of the formulated slurry. To comprehend the removal mechanism of the metals, potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) are employed. The same trend is encountered for both corrosion current density (Icorr) and film resistance (Rfilm)values resulting from Tafel and EIS. Based on the research conducted a plausible material removal mechanism is suggested. [ABSTRACT FROM AUTHOR]

Copyright of Electronic Components & Materials is the property of Electronic Components & Materials and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Topography of tungsten should be assured at a minimum through chemical mechanical planarization (CMP) in the metal gate structures (e.g., buried gates, replacement metal gates) and via contact in the middle of line (MOL) process for sub−7 nm semiconductor applications. However, excessive tungsten dissolution during the CMP process that results from high oxidizer concentrations and acidic atmospheres results in poor tungsten topography. In this study, we report a novel strategy to improve the tungsten topography by suppressing tungsten dissolution via coordination complex formations between picolinic acid and tungsten oxide. With 1.5 wt% picolinic acid for the inhibitor, the dissolution rate of tungsten was dramatically attenuated, and improved topography with a Ra value of 7.8 nm were demonstrated while validating CMP removal rate. [ABSTRACT FROM AUTHOR]

The effect of the two-way injection method during a copper chemical mechanical planarization (CMP) process was investigated. The two-way slurry-injection method has the advantage of not only preventing the degradation of the slurry, but also shortening the process time because the mixing process of the slurry and H2O2 is not required. Compared to the conventional method, the two-way injection method has an equivalent level of less than 9% in material removal rate. In particular, when injecting near the center of the pad, the influence according to the positions and flow rates of the two nozzles is also insignificant, so it deserves to be studied as a slurry-supply method.

Achieving high processing quality for chemical mechanical planarization (CMP) in semiconductor manufacturing is difficult due to the distinct process variations associated with this method, such as drift and shift. Run-to-run control aims to maintain the targeted process quality by reducing the effect of process variations. The goal of controller learning is to infer an underlying output–input reverse mapping based on input–output samples considering the process variations. Existing controllers learn reverse mapping by minimizing the total mapping error for sample data. However, this approach often fails to generate inputs for unseen target outputs because conditional input distributions on target outputs are not captured in the learning. In this study, we propose a controller based on a least squares generative adversarial network (LSGAN) that can capture the input distributions. GANs are deep-learning architectures composed of two neural nets: a generator and a discriminator. In the proposed model, the generator attempts to produce fake input distributions that are similar to the real input distributions considering the process variation features extracted using convolutional layers, while the discriminator attempts to detect the fake distributions. Competition in this game drives both networks to improve their performance until the generated input distributions are indistinguishable from the real distributions. An experiment using the data obtained from a work-site CMP tool verified that the proposed model outperformed the comparison models in terms of control accuracy and computation time. [ABSTRACT FROM AUTHOR]

The prediction of average Material Removal Rate (MRR) in Chemical Mechanical Planarization (CMP) process is regarded as a crucial research objective of Virtual Metrology (VM) for semiconductor manufacturing. In this paper, a novel VM model is proposed to predict MRR in CMP process based on the integration of Gaussian Process Regression (GPR) with a reference-based strategy. The proposed method estimates the similarity of the changing trends of the sensor traces using Maximum Mean Discrepancy (MMD) as a metric to extract reliable references for further prediction. The temporal information is also combined in the strategy by filtering the data samples by timestamps. Afterwards, GPR is used to fuse the reference data samples to predict the metrology with a confidence interval. Compared with the benchmarks in the recent literature, the proposed method, referred as MMD-GPR model, gives better prediction performance than ensemble learning methods and equivalent accuracy compared with Deep Neural Networks (DNN) but with a more efficient framework. [ABSTRACT FROM AUTHOR]

Copyright of Electronic Components & Materials is the property of Electronic Components & Materials and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Chemical mechanical planarization (CMP) is frequently used in semiconductor manufacturing to polish the surfaces of multiple layers in a wafer. The CMP uses a slurry that aids in fabricating a smooth surface by removing the excess materials. However, excessive use of slurry affects the environment and is expensive. Therefore, we propose a hybrid slurry supply system that combines ionization and atomization to reduce slurry consumption and improve the polishing quality. The proposed hybrid system atomizes the ionized slurry using electrolysis and a spray slurry nozzle. We compared the material removal rate (MRR) and polishing uniformity based on the slurry supply systems used in Cu and SiO2 non-patterned wafers. Additionally, the step height reduction and dishing were compared in the Cu-patterned wafers. The experimental analysis using the hybrid system confirmed a 23% and 25% improvement in the MRR and uniformity, respectively, in comparison with the conventional slurry supply system. This improvement can be attributed to the chemical activation and uniform supply of the ionized and atomized slurries, respectively. Moreover, a significant reduction was observed in dishing and pitch-size dependence. Furthermore, the proposed system prevents heat accumulation between the CMP processes, serving as a cooling system. [ABSTRACT FROM AUTHOR]

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Topography of tungsten should be assured at a minimum through chemical mechanical planarization (CMP) in the metal gate structures (e.g., buried gates, replacement metal gates) and via contact in the middle of line (MOL) process for sub−7 nm semiconductor applications. However, excessive tungsten dissolution during the CMP process that results from high oxidizer concentrations and acidic atmospheres results in poor tungsten topography. In this study, we report a novel strategy to improve the tungsten topography by suppressing tungsten dissolution via coordination complex formations between picolinic acid and tungsten oxide. With 1.5 wt% picolinic acid for the inhibitor, the dissolution rate of tungsten was dramatically attenuated, and improved topography with a Ra value of 7.8 nm were demonstrated while validating CMP removal rate.

In this study, we have developed a method to quantify inherent conditioner-to-conditioner variation using a mathematical model. Quantification of the lifetime of pad and conditioner in a chemical mechanical planarization (CMP) process using the model is also elucidated. Three conditioner types are selected based on their aggressiveness, and conditioning experiments are performed with five different conditioners of the same type to quantify the variation. Assuming exponential decay equation for pad wear rate (PWR), a simple model is developed and the model parameters, K and ${\lambda }$ , are derived in numeric form. K, a measure of conditioner aggressiveness, can be used as a metric to assess conditioner-to-conditioner variation, while ${\lambda }$ is a measure of conditioner lifetime. K and ${\lambda }$ values are also used to predict the failure mode of a CMP process (conditioner or pad failure mode). Pad groove depth and PWR curves of five different conditioners of the same type as a function of time are plotted along with upper and lower limits. The limiting factor (low PWR due to conditioner decay or excess pad removal) is identified to predict the failure mode. An example for each failure mode (A122 and 8031C1 conditioners for conditioner and pad failure modes, respectively) is presented. [ABSTRACT FROM AUTHOR]

The kinetics of dissolution of Co in alkaline glycine solution under anodic conditions was investigated. Potentiodynamic polarization and electrochemical impedance spectroscopic (EIS) data of Co dissolving in 0.1 M glycine solution at pH 10 were acquired. At low overpotentials, in the active region, the polarization current increases with potential, whereas at high overpotentials, in the passive region, it decreases with potential. Co surface exposed to the alkaline glycine solution was characterized using scanning electron microscopy and x-ray photoelectron spectroscopy, and based on the binding energies of the deconvoluted peaks, oxides, hydroxides, and Co-glycine complexes were identified as the likely species present on the surface. Impedance data was subjected to electrical equivalent circuit analysis, and a circuit with three Maxwell elements was found to model the faradaic impedance. Polarization and EIS data were also analyzed in the framework of mechanistic analysis, and a mechanism with four adsorbed intermediates is proposed. The proposed model captures the salient features of the polarization and EIS results. [ABSTRACT FROM AUTHOR]

The chemical mechanical planarization (CMP) performance of the Si-face and C-face 6H-SiC substrates were compared using potassium permanganate (KMnO4) as an oxidizer and the alumina (Al2O3) nanoparticles as the abrasive particles over a pH range from 2 to 10. The results indicate that there was a significant difference in the CMP performance between Si-face and C-face 6H-SiC substrates, indicating that the CMP process was sensitive to the surface polarity of the 6H-SiC substrates. A higher material removal rate (MRR) was obtained during the CMP of C-face 6H-SiC substrates, as compared to that of Si-face 6H-SiC substrates. The maximum MRR of the C-face 6H-SiC substrates was reached to 6412 nm/h with an average surface roughness Ra of 0.54 nm when using the slurries containing 2 wt% alumina nanoparticles, 0.05 M KMnO4 at pH 2. While the Si-face 6H-SiC substrates was 1554 nm/h with an average surface roughness Ra of 0.53 nm. Furthermore, the polishing mechanism was also discussed based on the MRR, atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS) analysis of the dipped and polished 6H-SiC surfaces. [ABSTRACT FROM AUTHOR]

Technologies in semiconductor industry have been developed into a three-dimensional multilayer wiring for high integration of devices. Chemical mechanical planarization (CMP) process is one of the key technologies for achieving multilayer wiring, which enables global planarization. In addition, highly integrated devices can be realized by increasing the depth of focus in the photolithography process. However, in the inter-layer dielectric (ILD) CMP of the transistor, the uppermost oxide layer has the step due to the arrangement of the devices. The ideal material removal mechanism is to gradually remove materials from the top of the step height which allows for global planarization. However, in the CMP of the patterned wafers, simultaneous polishing of the upper and lower layers occurs when the step height reaches a certain height. This means that the polishing is strongly dependent on the structural characteristics of the pattern. Especially, the difference in the material removal rate depending on the pattern density acts as a constraint in terms of device layout. Therefore, it is essential to develop an accurate prediction model of material removal rate as a function of pattern density, size and arrangement. This study aims to define the mathematical planarization model according to contact mode between a polishing pad and patterned wafer. Considering that the real contact area between the actual polishing pad and the wafer is about 1 %, the mathematical model is derived based on the microscopic deformation of the pad asperities, not the macroscopic deformation of the bulk pad. Finally, we describe the verification between the theoretical material removal rate model and step height reduction and the actual CMP results. The root mean square error of the upper layer material rate, the lower material removal rate, and the step height reduction were 24.59 nm/min, 22.03 nm/min and 22.6 nm, respectively. Compared with the previous studies, the new model of this study improved the error by up to 50.9 %. [ABSTRACT FROM AUTHOR]

In semiconductor manufacturing, the chemical mechanical planarization (CMP) process produces higher thickness variability in the edge area of the wafer than that in the center area due to the characteristics of the polishing operation. To address this problem, advanced CMP equipment includes a function that controls the removal rate of each area. However, to take full advantage of this capability, effective advanced process control systems must be implemented with a virtual metrology (VM) model. However, the prediction performance of the VM model often deteriorates due to process drift. Here, we present a deep learning-based VM model that demonstrates high performance in the presence of nonlinear process drift. The proposed model combines a recurrent neural network and a convolutional neural network to extract time-dependent and time-independent features. A two-stage model training method is proposed that alternately updates the weights of each network to improve prediction performance. In the experiments using on-site CMP process data, the performance of the deep learning models exceeded that of standard machine learning models. The proposed model showed an 8.48% decrease in process variability relative to the best-performing machine learning model, which was elastic nets. [ABSTRACT FROM AUTHOR]

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 Si 3 N 4 film in the advanced STI CMP process. The selective adsorption of PVA onto the Si 3 N 4 film lowers its removal rate by suppressing the hydrolysis reaction via hydrogen bonding while the removal rate of SiO 2 film remains constant. Moreover, the uniformity of SiO 2 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 SiO 2 film is supplemented by 96.8 % simultaneously. [Display omitted] • Polyvinyl alcohol (PVA), a nonionic polymer, was proposed as a passivation agent for Si 3 N 4 film during advanced STI CMP. • PVA adsorption onto the Si 3 N 4 via hydrogen bonding suppresses hydrolysis of Si 3 N 4 , resulting in drastic removal reduction. • The uniformity of SiO 2 removal was improved due to the increase in wettability. • The removal selectivity between SiO 2 and Si 3 N 4 films has been improved to 113.4:1. [ABSTRACT FROM AUTHOR]

• A malonic acid solution at neutral-pH is used for post CMP cleaning of copper. • Brushing combined with malonic acid's chemistry removes ∼ 97 % of CMP residues. • Relative roles of chemistry and brush scrubbing in post CMP cleaning are examined. • Tribology-coupled electroanalysis effectively probes post CMP cleaning mechanism. • An electrochemical method is demonstrated to assess post CMP cleaning efficiencies. Post-CMP cleaning (PCMPC) is used in the fabrication of integrated circuits to remove unwanted residues of chemical mechanical planarization (CMP) from wafer surfaces. PCMPC combines (electro)chemical surface reactions in a cleaning solution with mechanical scrubbing by a brush. Using tribo-electrochemical protocols, the present work simultaneously probes the (electro)chemical and mechanical functions of PCMPC and determines the corresponding cleaning efficiency. Employing malonic acid as a cost effective, environmentally compatible cleaning solution for copper, it is demonstrated how this electroanalytical approach can facilitate the tasks of developing and evaluating effective PCMPC solutions. The results indicate that malonic acid in a pH neutral environment removes Cu-oxides and their co-adsorbed organic residues of CMP. A detailed electrochemical procedure is illustrated for quantifying the residue removal efficiency of PCMPC, and the role of brush scrubbing in the overall cleaning process is identified. [ABSTRACT FROM AUTHOR]

Micron-sized subwavelength structured photonic sensors could allow critical thermo-mechanical phenomena in manufacturing processes to be monitored, while offering tremendous advantages. To implement these novel sensors into real manufacturing processes, the microring sensors can be embedded at critical locations in metallic structures, which are heavily used in hostile manufacturing environments. This paper presents our research progress on fabrication, embedding, and application of integrated mciroring sensors. For fundamental study, microring resonators have been microfabricated on-chip with silicon-on-insulator wafer substrates. The fabricated optical resonators offer Qs on the order of 20,000, enabling significantly improved temporal sensing and detection of high-frequency strain variation. A new approach to fabricate metal embedded photonic structures was developed. Various thin film photonic materials were studied for embedded microphotonic sensors. Effect of process parameters on refractive index of silicon nitride/silicon oxynitride films was studied. Issues on design and implementation of micro sensors for Chemical Mechanical Planarization (CMP) were studied and a grafting process was designed to embed micro sensors into polishing pads. A preliminary design for the sensor and its installation into a continuous casting mold was also developed.