Addressing Dynamic Process Changes in High Volume Plasma Etch Manufacturing by Using Multivariate Process Control

Parkinson, Blake R; Lee, Hyung; Funk, Merritt; Prager, Daniel; Yamashita, Asao; Sundararajan, Radha; Edgar, Thomas F.

doi:10.1109/tsm.2010.2041293

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…(3) is obtained, an optimization problem with constraints can be solved with a quadratic objective function described in Eq. (4) in order to obtain optimum MVs in MIMO control [12][13][14][15] J ¼ min…”

Section: Optimization Problems With Oes Datamentioning

confidence: 99%

Systematic procedure to optimize chamber seasoning conditions with optical emission spectroscopy in plasma etching

Baek

Seong

Min

et al. 2014

Journal of Vacuum Science &Amp; Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phen

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Environmentally benign etching process of amorphous silicon and tungsten using species evaporated from polytetrafluoroethylene and fluorinated ethylene propylene As chamber conditions gradually change with wafer processing, periodic wet cleaning is an inevitable event in semiconductor manufacturing. Since the chamber conditions are initialized during the wet cleaning, a chamber conditioning process called chamber seasoning follows the wet cleaning step. In this paper, a systematic procedure to optimize chamber seasoning for plasma etching is proposed, and the effectiveness is demonstrated in a semiconductor manufacturing environment. In order to quantitatively analyze plasma conditions for chamber seasoning and to achieve the optimum conditions objectively, a normalization technique for optical emission spectroscopy called a selfbackground normalization technique and a computational optimization process is suggested. By applying the optimized chamber seasoning conditions, a plasma reactor which is suffering from a serious etch rate drift after wet cleaning returns to a production ready status. Also, the etch rate of Si, which is an index for production readiness, is perfectly matched to 37.8 A/s for production. Hopefully, the proposed methodology in this paper will be disseminated to semiconductor manufacturers who experience similar issues after wet cleaning.

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Section: Optimization Problems With Oes Datamentioning

confidence: 99%

Systematic procedure to optimize chamber seasoning conditions with optical emission spectroscopy in plasma etching

Baek

Seong

Min

et al. 2014

Journal of Vacuum Science &Amp; Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phen

Self Cite

View full text Add to dashboard Cite

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“…(7) in order to obtain optimum MVs in MIMO control [12][13][14] J ¼ min (6) by using the data from design of experiment, an optimization problem with constraints can be solved with a quadratic objective function described in Eq.…”

Section: Dynamic Optimization Techniques For Multiple Input Multipmentioning

confidence: 99%

Multiple input multiple output controller design to match chamber performance in plasma etching for semiconductor manufacturing

Baek¹,

Han²,

Choi³

et al. 2013

Journal of Vacuum Science & Technology B, Nanotechnology an

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Articles you may be interested inSystematic procedure to optimize chamber seasoning conditions with optical emission spectroscopy in plasma etching J. Vac. Sci. Technol. B 32, 022203 (2014); 10.1116/1.4865909 Inductively coupled plasma etching of through-cell vias in III-V multijunction solar cells using SiCl4/Ar Formation and removal of composite halogenated silicon oxide and fluorocarbon films deposited on chamber walls during plasma etching of multiple film stacksIn semiconductor manufacturing, multiple chambers utilized for the same process step often experience performance variation. This chamber to chamber performance variation has affected the yield of wafers, but there are no standard procedures to reduce them in semiconductor manufacturing. This paper introduces chamber matching in plasma etching as one of the core issues in semiconductor manufacturing and suggests a step-by-step procedure to address chamber matching issues. A brief review of two approaches, fault detection and classification and equipment control, is given and a step-by-step procedure of the equipment control approach is introduced. To design a multiple input-multiple output controller, a decomposed etch rate map makes it possible to analyze etch rate performance between chambers and to define controlled variables. Optimum variable selection techniques, such as singular value analysis and relative gain array methods, and dynamic optimization with constraints are suggested in this paper. In addition, a performance matching index (PMI) is introduced to calculate performance of chambers in terms of both etch rate and wafer yield to verify the matching capability of our approach. PMI is a quantitative index, which calculates performance of two chambers and lower value is better. By applying our approach, the performance of a worst chamber reaches to that of a golden chamber, resulting in PMI improvement from 100.7 to 31.6. The methodology of chamber matching introduced in this paper should be useful to semiconductor manufacturers impacted by chamber matching issues.

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“…The variation in different chambers is caused from the behavior of different etching equipments, which means the bias is in the chamber. However, most of the existing studies for advanced process control (APC) have investigated the CD variation reduction regarding the within-wafer, W2W and L2L CD control (El Chemali et al 2003;Williams et al 2005;Zhang, Poolla, and Spanos 2008;Parkinson et al 2010). Little research addresses the CD control between lots etched in different chambers to compensate the bias in etching process.…”

Section: Introductionmentioning

confidence: 99%

Manufacturing intelligence for determining machine subgroups to enhance yield in semiconductor manufacturning

Chien

Chen

Hsü

et al. 2011

Proceedings of the 2011 Winter Simulation Conference (WSC)

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Linewidth control is a critical issue for yield enhancement in semiconductor manufacturing. Most of the existing techniques such as run-to-run control have been developed to control the critical dimension (CD) in photolithography and etching process. However, few studies have addressed the tool behavior that would also affect the result of CD in etching process and the etch bias that is the CD difference between photolithograph and etching process. This study aims to propose a manufacturing intelligence (MI) approach to develop dispatching rules for etching tool in order to reduce the variation of critical dimension measured after etching process and determine the machine subgroups for compensating the etching bias. An empirical study was conducted to estimate the validity of proposed approach and the results showed practical viability of this approach. INTRODUCTIONWith the shrinking feature size of integrated circuits (ICs) in advanced technology, the tolerance of critical dimension (CD) becomes tight and slight. Photolithography and etching are two main process for determining CD in semiconductor manufacturing. The CD is the minimum width of patterned line or the distance between two pattern lines. In particular, the measurement of CD after exposure and development in photolithography process is developed critical dimension (DCD) and the CD measured after etching process called etched critical dimension (ECD), which represents the final linewidth for each layer. The ECD directly affects semiconductor device performance. In order to assure product quality and process yield, ECD must be controlled within the tolerance precisely. ECD variation may occur within a wafer, from wafer-to-wafer (W2W), from lot-to-lot (L2L) and between lots etched in different chambers. The CD variation occurred within a wafer is usually represented by the uniformity. It is generally a consequence of reactor design and determined by factors such as gas flow pattern, chamber symmetry and plasma source configuration. Wafer-to-wafer and lot-to-lot CD variation may occur because of poor repeatability at equipment, process and photolithography on incoming 1893 978-1-4577-2109-0/11/$26.00 ©2011 IEEE

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Addressing Dynamic Process Changes in High Volume Plasma Etch Manufacturing by Using Multivariate Process Control

Cited by 6 publications

References 3 publications

Systematic procedure to optimize chamber seasoning conditions with optical emission spectroscopy in plasma etching

Systematic procedure to optimize chamber seasoning conditions with optical emission spectroscopy in plasma etching

Multiple input multiple output controller design to match chamber performance in plasma etching for semiconductor manufacturing

Manufacturing intelligence for determining machine subgroups to enhance yield in semiconductor manufacturning

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