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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 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]

A diamond disk is indispensable to dressing chemical mechanical planarization (CMP) polishing pads. Current CMP diamond disks contain numerous diamond grits (approximately 10,000-25,000) on the flat surface of a metal substrate. Although a diamond disk has a regular distribution of grits on its surface, their tips are not of uniform height. Hence, only a small proportion of the grits can penetrate the flexible pad surface, and a large number of grits remain unused on the disk. In this work, we developed a novel technique to find and remove tall diamond grits on a diamond disk to improve the leveling of the diamond tips and increase the number of working grits. We examined an original diamond disk and disks in which three, five, and seven tall diamond grits were removed. We then evaluated the surface characteristics and pad dressing rates (PDRs) as well as the removal rate for a dielectric oxide film dressed with these diamond disks after the removal of the tall grits. We compared the results with those obtained for the original diamond disk. The removal of the tall diamond grits facilitated diamond leveling and helped increase the number of working grits. The PDR decreased as the number of tall grits removed increased. The experimental results revealed that, after the removal of the tall grits, the diamond disk exhibited better non-uniformity and a lower pad consumption rate without damaging the local circuitry. In addition, the lifetime of the pad and disk was prolonged, which should lower the cost of the CMP process. [ABSTRACT FROM AUTHOR]

In this paper, the effect of slurry injection position on removal rate and nonuniformity was experimentally investigated on a 300-mm polisher developed by the authors, and the effect of slurry injection position on slurry fluid pressure distribution during polishing was also measured by a novel in situ fluid pressure measurement system integrated in the polisher. In situ pad conditioning was employed in all experiments. The results showed that removal rate, nonuniformity, and fluid pressure distribution were influenced dramatically by the slurry injection position. The slurry injection position determines the slurry distribution, and therefore, the removal rate profile and fluid pressure distribution were affected. [ABSTRACT FROM AUTHOR]

Chemical mechanical planarization (CMP) is the major manufacturing step used to planarize semiconductor wafers and obtain mirror surface finish. In CMP, diamond disk pad conditioning is traditionally employed to restore pad planarity and surface roughness. The conditioning tool typically consists of a metal disk with one side embedded with protruding diamond grits (abrasives). The conditioner design has a significant effect on the pad conditioning process and hence the wafer planarization process. This paper proposes the application of engineering optimization methods such genetic algorithm to the conditioner design problem for the first time. A new metric to evaluate conditioning performance based on the conditioning density generated by a specific conditioner design is developed. The metric is applied in a genetic algorithm to optimize conditioner design parameters (including geometric arrangement of diamonds, grit density and disk size). The model searches for the design parameters that produce a desired CMP pad surface texture. Results show that the model can effectively serve as a platform to evaluate and tune conditioner design for different applications in CMP. [ABSTRACT FROM AUTHOR]

The surface planarization in the chemical mechanical planarization is mainly realized by the mechanical effect which depends on the micro-tribology behavior of nanoparticles. The material removal rate is decreased and micro-scratch is generated in the wafer surface due to the agglomeration of particles. The slurry particles hold definite energy level and microstructure which makes the classical materials processing theory unable to give reasonable explanation about this process. Large-scale classical molecular dynamic simulation of interaction among nanoparticles and solid surface has been carried out to investigate the evolvement mechanism of particle cluster and surface planarization. It is showed that the particle aggregation behavior is a size-dependent process because of the high ratio of surface atoms to bulk atoms which leads to the active reactivity and catalytic activity of nanoparticles. The substrate materials (nanoparticle) become transforming from low-energy stable system to high-energy unstable system after the two-body collision which is favorable for aggregation process. There exists obvious geometry boundary between the particle elements in the large aggregates and only the dendritic aggregates formed. The larger particles have definite microstructure and additional internal degree of freedom which can be used to take up, store, and transfer energy, thus lower the activity of chemical reaction which weaken the particle aggregation process. [ABSTRACT FROM AUTHOR]

Here we use two-dimensional models of fluid film lubrication and contact mechanics to calculate the contact stress and fluid (i.e., slurry) pressure distributions on the wafer-pad interface in chemical mechanical planarization (CMP). In particular, the effective rigidity of the wafer (determined by the wafer carrier structure), the retaining-ring width and its back pressure are taken to be the design parameters. The purpose is to study the synergetic effects of such parameters on the contact stress uniformity, which directly affects the spatial uniformity of the material removal rate on the wafer surface. Our numerical results indicate that, for a given wafer rigidity, one may choose the retaining-ring width and back pressure to minimize the contact stress non-uniformity (NU). Also, the resulting minimum NU decreases with the effective wafer rigidity, suggesting that it is beneficial to use a soft (e.g., floating-type) wafer carrier. Moreover, for a soft wafer carrier, it is demonstrated that using a multi-zone wafer-back pressure profile is even more effective in reducing NU. [ABSTRACT FROM AUTHOR]

The objective of this work is to tackle the challenges of monitoring and detecting subtle process changes in chemical mechanical planarization (CMP), an ultraprecision manufacturing process. Monitoring ultraprecision processes is usually of difficulty due to their innate complexity and low signal-to-noise ratio in sensor signals. Especially for subtle signal variations during small process changes, the conventional statistical process control charts could fail to detect such process anomalies from the sensor signals in the time domain. In this paper, frequency spectra representation of the microelectromechanical systems (MEMS) vibration sensor signals during subtle process changes is investigated, and the signal patterns uncovered by frequency spectra are utilized to formulate a machine learning–driven in situ process monitoring approach to detect process anomalies in CMP. The proposed approach overcomes the obstacles of differentiating subtle signal changes by transforming them into the frequency domain with Fourier transform and Hilbert-Huang transform and classifying the resulted frequency spectra with random forest. Based on frequency analysis, it can unveil the differences in the signals obscured in the time domain and suppress the high-frequency noise. Consequently, the presented machine learning–driven in situ process monitoring approach detects process anomalies by differentiating the deviated frequency spectra with machine learning. It is validated on our experimental CMP testbed for anomaly detection, and outperforms three benchmark statistical process control charts. For instance, it detects a slurry shutoff anomaly in CMP about ten times faster than the benchmark methods. [ABSTRACT FROM AUTHOR]