Chemical Mechanical Polishing Method and Practice

Cadien, Ken; Nolan, L.

doi:10.1016/b978-0-12-812311-9.00010-4

Cited by 20 publications

(8 citation statements)

References 43 publications

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“…To date, a variety of ultra-precision polishing methods such as uid polishing [6,7], electrochemical polishing [8], and chemical-mechanical polishing (CMP) have been applied to obtain high-quality defect-free ceramic surfaces [5,9]. Among them, CMP is considered to be a powerful fabrication technology for removing material by combining chemical and mechanical action together, leading to the achievement of global planarization and ultra-smooth surfaces with no defects [5,10]. Moreover, it has been widely applied in the fabrication of semiconductor devices and planarization of materials such as silicon-on-insulators [11], sapphire [12], and silicon carbide (SiC) [13].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, appropriate selection of polishing slurry is signi cantly important as it can provide a unique polishing environment between the workpieces and the polishing pad, involving both chemical and mechanical action for better polishing, and leading to high e ciency and selective removal [17]. Polishing slurries are usually composed of abrasives, oxidizer, pH regulator, and solvent [5,10]. For glass and silicon nitride (Si 3 N 4 ) ceramic workpieces, CMP has been successfully applied by using water solution, under polishing environment with pH 7-9, and abrasive with a hardness of approximately 7 Mohs (such as zirconia (ZrO 2 ) and ceria) [18].…”

Section: Introductionmentioning

confidence: 99%

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WITHDRAWN: Ultra-Precision Polishing Method of Polymer-Derived Amorphous SiAlCN Ceramics

Chen

Cao

Wang

2020

Preprint

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The amorphous and heterogeneous microstructures of polymer-derived ceramics (PDCs) make it challenging to uniformly polish them without the appearance of obvious polishing defects. In this study, the surface characteristics of polymer-derived silicoaluminum carbonitride (SiAlCN) ceramics were investigated after mechanical polishing and chemical–mechanical polishing (CMP). The results showed the generation of polishing defects including scratches, pits, and comet tails after mechanical polishing; nonetheless, the surface quality was largely improved after CMP, providing an ultra-smooth surface (Ra = ~0.25 nm). The evolution of the surface morphology was characterized in detail and X-ray photoelectron spectroscopy was used to analyze the characteristics of chemical bond before and after the CMP. The polishing slurry dominated the corrosion process during CMP, inducing the oxidation of the SiCxNy phase; nonetheless, the so-formed oxidation products and carbon phase were removed by mechanical action. The obtained ultra-high smooth surface can promote practical application in the field of PDCs-based sensors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Ultra-Precision Polishing Method of Polymer-Derived Amorphous SiAlCN Ceramics

Chen

Cao

Wang

2020

Preprint

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“…The next important step consists of chemical mechanical planarization or chemical mechanical polishing (CMP) is a process in which topographical irregularities can be removed from wafers with a combination of chemical and mechanical (or abrasive) polishing in order to obtain the smoothest surface possible [46,47]. The process is usually used to planarize oxide, polysilicon, or metal layers in order to prepare them for the The second main step is the crystal growth of monocrystalline silicon and growth of multicrystalline ingots [43].…”

Section: Semiconductor Manufacturing Processmentioning

confidence: 99%

A Review of Data Mining Applications in Semiconductor Manufacturing

Espadinha-Cruz

Godina

Rodrigues³

2021

Processes

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For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.

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“…The planarization and polishing of the wafer is achieved through a mechanical abrasion and chemical etching. 30 One of the critical steps where CMP is used is when oxide is polished away until silicon nitride is fully exposed rendering the shape of the circuit desired by engineers. In the CMP process, scratches formation originates 31 when the mechanical CMP action process is attributed to the polishing pad interactions with a slurry.…”

Section: Defect Datamentioning

confidence: 99%

“…In CMP, a wafer is placed on a carrier and pressed into contact with a slurry film on a polishing pad. The planarization and polishing of the wafer is achieved through a mechanical abrasion and chemical etching 30 . One of the critical steps where CMP is used is when oxide is polished away until silicon nitride is fully exposed rendering the shape of the circuit desired by engineers.…”

Section: Defect Datamentioning

confidence: 99%

Identification of spatial defects in semiconductor manufacturing

Borgoni

Galimberti

Zappa

2021

Appl Stoch Models Bus & Ind

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In this article, we investigate the occurrence of defects in integrated circuit fabrication and show how spatial analysis can be effective in grasping and representing spatial regularities in defect patterns on the silicon supports, called wafers, used to produce microchips. Defects occurring on the wafer surface are the main cause of yield loss in the semiconductor industry; hence, to promptly detect an excess of defects and identify their spatial structure is crucial to the entire fabrication process. To address this hard-to-solve problem, this article proposes a concatenation of different methods, namely, a control chart, a clustering algorithm, and a graphical tool. First, a control chart based on the p-value of an appropriate test recognizes spatially structured defects on the wafer area.Then a clustering procedure grounded on the minimum spanning tree algorithm is adopted to identify those regions more prone to defect occurrences. Finally, alpha-shapes are employed to display their shape effectively. The suggested procedure proves to be extremely fast and effective, allowing its implementation in-line during the fabrication process. This provides a great advantage in modern microelectronics where items tend to be highly specialized and often produced in small lots. In particular, due to the Monte Carlo nature of the procedure, the control chart proposed hereafter does not require gold standard data to be set. This is particularly advantageous for small lot production, which is typically limited in time and does not permit to collect long time series of the charting statistics. K E Y W O R D Salpha-shapes, integrated circuits fabrication, minimum spanning tree algorithm, p-value control charts INTRODUCTIONSpatial statistics has long played a fundamental role in environmental, agriculture, and ecological studies as well as in economics, epidemiology, and other human sciences. Less usual are applications in industry and technology where the spatial dimension is often crucial to assess the quality of production processes. In this article, we investigate the spatial structure of defects occurring in integrated circuit fabrication. Integrated circuits are built through several different physical and chemical steps that are performed on thin silicon slices of a few 878

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Chemical Mechanical Polishing Method and Practice

Cited by 20 publications

References 43 publications

WITHDRAWN: Ultra-Precision Polishing Method of Polymer-Derived Amorphous SiAlCN Ceramics

WITHDRAWN: Ultra-Precision Polishing Method of Polymer-Derived Amorphous SiAlCN Ceramics

A Review of Data Mining Applications in Semiconductor Manufacturing

Identification of spatial defects in semiconductor manufacturing

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Chemical Mechanical Polishing Method and Practice

Cited by 20 publications

References 43 publications

WITHDRAWN: Ultra-Precision Polishing&nbsp;Method&nbsp;of&nbsp;Polymer-Derived&nbsp;Amorphous SiAlCN Ceramics

WITHDRAWN: Ultra-Precision Polishing&nbsp;Method&nbsp;of&nbsp;Polymer-Derived&nbsp;Amorphous SiAlCN Ceramics

A Review of Data Mining Applications in Semiconductor Manufacturing

Identification of spatial defects in semiconductor manufacturing

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Product

Resources

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WITHDRAWN: Ultra-Precision Polishing Method of Polymer-Derived Amorphous SiAlCN Ceramics

WITHDRAWN: Ultra-Precision Polishing Method of Polymer-Derived Amorphous SiAlCN Ceramics