Optimization of plasma etch processes using evolutionary search methods with<i>in situ</i>diagnostics

Al-Kuzee, Jafar; Mizutani, Takashi; Goodyear, A.; Nolle, Lars; Hopgood, Adrian A.; Picton, Philip; Braithwaite, Nicholas

doi:10.1088/0963-0252/13/4/010

Cited by 16 publications

(9 citation statements)

References 17 publications

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“…This data demonstrates that the nitrogen etch process can be used to fabricate topography on a PS-b-P4VP BCP thin film comparable to the ethanol reconstruction process. Based on literature reports, it is anticipated that further work on optimising the etch process using plasma diagnostic tools could further enhance the etch selectivity and work towards reaching the standards of wet etching across larger sample areas without any defects [46][47][48][49].…”

Section: Resultsmentioning

confidence: 99%

Nitrogen reactive ion etch processes for the selective removal of poly-(4-vinylpyridine) in block copolymer films

et al. 2018

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Self-assembling block copolymer (BCP) patterns are one of the main contenders for the fabrication of nanopattern templates in next generation lithography technology. Transforming these templates to hard mark materials is key for pattern transfer and in some cases, involves selectively removing one block from the nanopattern. For poly(styrene)-block-poly(4-vinylpyridine) (PS-b-P4VP), a high χ BCP system which could be potentially incorporated into semiconductor nanofabrication, this selective removal is predominantly done by a wet etch/activation process. Conversely, this process has numerous disadvantages including lack of control and high generation of waste leading to high cost. For these reasons, our motivation was to move away from the wet etch process and optimise a dry etch which would overcome the limitations associated with the activation process. The work presented herein shows the development of a selective plasma etch process for the removal of P4VP cores from PS-b-P4VP nanopatterned film. Results have shown that a nitrogen reactive ion etch plasma has a selectivity for P4VP of 2.2:1 and suggest that the position of the nitrogen in the aromatic ring of P4VP plays a key role in this selectivity. In situ plasma etching and x-ray photoelectron spectrometry measurements were made without breaking vacuum, confirming that the nitrogen plasma has selectivity for removal of P4VP over PS.

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Section: Resultsmentioning

confidence: 99%

Nitrogen reactive ion etch processes for the selective removal of poly-(4-vinylpyridine) in block copolymer films

et al. 2018

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“…In the performed etching processes, the variation of total flow rate of all trials was less than 10% of minimum total flow rate used and, consequently, also the variation of pressure was less than 10%. Summarizing, in this overall experimental setting we can neglect in a first approximation the variations of the local ion flux and of the average ion energy [6], [14] when the flow rate of the chemically active components changes.…”

Section: Experimental Methodsmentioning

confidence: 99%

Fluorocarbon Chemistry: A 0-Dimensional Model for Oxide and Nitride Dry Etching

Garozzo

Colombo

Lombardo

et al. 2015

IEEE Trans. Semicond. Manufact.

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In this work, a 0-dimensional model for the understanding of dry etching characteristics in silicon oxide and nitride materials is reported. The model is applied to analyze the etching performances in a design of experiments where gas mixtures are varied in the fluorocarbon chemistry typical of the "protected sidewall" regime. The modeling analysis of flat sample etching allows for an accurate tuning of the selectivity's behavior, and can be generalized to deal with patterned samples. In particular, we apply a phenomenological technique to transform the equipment parameters in the microscopic quantities ruling the reagent-surface interactions. The correct prediction of the etch rate trends in flat samples and the sensibility to the different etching mechanisms for the nitride case demonstrate the reliability of the proposed approach.Index Terms-plasma etching, protected sidewall RIE, fluorocarbon plasma 0894-6507 (c)

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“…GA is one of the most popular search algorithms used by scholars in areas such as industrial engineering to attain a more accurate and optimal optimization result [5], [6]. For example, GA approach has been used to optimize industrial processes, reduce production costs, minimize manufacturing times and increase flexibility of machine selection [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Computational Intelligence Approach for Process Parameter Settings Using Knowledge Representation

Lau¹

2015

LNSE

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Abstract-This study proposes a fuzzy approach which integrates fuzzy rule sets in a chromosome. To enhance the functionality and capability of the fuzzy set, Genetic Algorithms (GA) technique is incorporated to produce a better and improved fuzzy set which is able to generate the expected result. Past data were selected to create the chromosomes and form the primary population set. This approach capitalizes on the merits of both techniques and offsets the drawbacks of them which may undermine the performance. This research signifies the hybrid approach to identify the optimal criteria for process control in order to achieve the target of the whole operations with an innovative methodology that has not been covered adequately to-date. A case example has been conducted to validate the practicality of the approach and the outcome demonstrated that the proposed approach is able to achieve the results as expected.Index Terms-Genetic algorithms, demand uncertainty, supply chain management, fuzzy logic model. I. INTRODUCTIONKnowledge and innovation are very important for potential business process improvements in a supply chain network with the increasing trends of short product life cycle and mass customization [1]. In order to improve the business process, lots of scholars focus on the process capability studies recently. The product features are measured and analyzed to determine the process ability process in order to meet product specifications [2]. Providing products and service in time with good quality is vital for business success. But how can a company optimize business processes to provide better products and service? Many scholars put great efforts in exploring the artificial intelligence (AI) techniques, such as intelligent systems [3].How to develop a next generation intelligent system emerges as a new research interest for AI research. For example, some scholars focus on the integration of Genetic Algorithms (GA) and Fuzzy Logic approach [4]. However, the application of this hybrid approach is limited. This study proposes a Fuzzy GA approach to integrate fuzzy rule sets and their associated membership function sets into GA approach. With this hybrid approach, both crisp and fuzzy data can be integrated into one chromosome which is impossible in traditional GA approach.The hybrid fuzzy-GA approach proposed in this study is conducted as follows. First, process parameter is encoded as a fuzzy rule sets and the fuzzy membership function is Manuscript received February 16, 2014; revised May 6, 2014. Henry C. W. Lau is with School of Business, University of Western Sydney, Australia (e-mail: H.lau@uws.edu.au).created for the fuzzy rule sets. Past data are selected to create the chromosomes and form the primary population set. Second, GA technique is used to produce a better and improved fuzzy set which is able to generate the expected result.This paper has 7 sections. In Section II, literature concerning the fuzzy logic approach, GA approach, and their related applications are presented. Section III is about t...

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Optimization of plasma etch processes using evolutionary search methods within situdiagnostics

Cited by 16 publications

References 17 publications

Nitrogen reactive ion etch processes for the selective removal of poly-(4-vinylpyridine) in block copolymer films

Nitrogen reactive ion etch processes for the selective removal of poly-(4-vinylpyridine) in block copolymer films

Fluorocarbon Chemistry: A 0-Dimensional Model for Oxide and Nitride Dry Etching

Computational Intelligence Approach for Process Parameter Settings Using Knowledge Representation

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