Modeling of defect generation during plasma etching and its impact on electronic device performance—plasma-induced damage

Eriguchi, Koji

doi:10.1088/1361-6463/aa7523

Cited by 40 publications

(42 citation statements)

References 113 publications

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“…The thicknesses of the surface and interfacial layer are denoted as d SL and d IL , respectively, thus, d dam = d SL + d IL . This model was confirmed to be valid for the PPD analysis in the present d dam range and is widely used for in‐line monitoring . The details of the PPD characterizations have been reported elsewhere …”

Section: Ppd Model For Progressive Damaged Layer Thicknesssupporting

confidence: 66%

“…Figure shows the detailed structures of the damaged region under various ion doses D ion1 , D ion2 , and D ion3 , accompanied by the result by a molecular dynamics (MD) simulation on the top. Some of the MD simulations results regarding the damaged layer formation have been presented elsewhere . The MD simulation result shows the presence of Ar atoms deep in the Si substrate and the interfacial region with knocked‐on Si, O, and Ar atoms.…”

Section: Ppd Model For Progressive Damaged Layer Thicknessmentioning

confidence: 96%

“…Interstitial species such as injected Ar and knock‐on Si or O atoms in the final Si substrate play a role as latent defects. Hence, the trade‐off relationship between d R and the areal remaining defect density N dam is written as

N_{dam} false(E_{ion} false) goodbreakinfix= \int_{d_{normalR}}^{\infty} n_{dam} (E_{ion}, x) d x .

…”

Section: Optimization Of Plasma Parameters—maximizing Etch Ratementioning

confidence: 99%

“…Some of the MD simulations results regarding the damaged layer formation have been presented elsewhere. [2,3] The MD simulation result shows the presence of Ar atoms deep in the Si substrate and the interfacial region with knocked-on Si, O, and Ar atoms. By a series of collisions, the defects are created and the mechanism is accumulated in accordance with D ion as illustrated in Figure 2b.…”

Section: Ppd Range Theorymentioning

confidence: 99%

“…[1] Despite advancements in plasma processing, the degradation of material properties due to plasma exposure-plasma process-induced damage (PID)-has become a key issue. [2][3][4] In general, PID is classified on the basis of the mechanisms of its generation such as "physical damage," [5][6][7][8] "charging damage," [4,[9][10][11][12] and "radiation damage." [13][14][15][16][17] Physical damage is induced by high-energy ion bombardment on Si substrates or other material surfaces.…”

Section: Introductionmentioning

confidence: 99%

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Improvement of the plasma‐induced defect generation model in Si substrates and the optimization design framework

Eriguchi

Hamano

Urabe

2019

Plasma Processes & Polymers

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Defect generation in Si substrates during plasma processing—plasma‐induced physical damage (PPD)—has been principally modeled on the basis of the projection range of incident ions. In this paper, first, the ion dose (Dion) dependence of the thickness of a damaged layer (ddam) was implemented in the conventional PPD range model. An improved PPD range model was proposed to describe the dose evolution of ddam in addition to the ion energy (Eion) dependence of ddam. Then, the model prediction results were compared with the experimentally obtained ddam after Ar plasma exposure. A good agreement was found, which implied that plasma process designs should be performed by taking into account both the Dion and Eion dependences of ddam. Finally, a methodology of plasma process design based on the improved PPD range model was presented. The methodology was regarded as an optimization problem under constraints imposed by PPD criteria. Two scenarios—optimization problems—were demonstrated, where the etch rate was maximized under the following two constraints: one is by considering the acceptable ddam, and the other, the recessed Si depth and latent defect density with its trade‐off relation. The results suggested that minimizing Eion rather than lowering the flux of incident ions is essential for the above scenarios.

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Section: Ppd Model For Progressive Damaged Layer Thicknesssupporting

confidence: 66%

Section: Ppd Model For Progressive Damaged Layer Thicknessmentioning

confidence: 96%

N_{dam} false(E_{ion} false) goodbreakinfix= \int_{d_{normalR}}^{\infty} n_{dam} (E_{ion}, x) d x .

…”

Section: Optimization Of Plasma Parameters—maximizing Etch Ratementioning

confidence: 99%

Section: Ppd Range Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improvement of the plasma‐induced defect generation model in Si substrates and the optimization design framework

Eriguchi

Hamano

Urabe

2019

Plasma Processes & Polymers

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Effects of Ion Energy and Density on the Plasma Etching‐Induced Surface Area, Edge Electrical Field, and Multivacancies in MoSe₂ Nanosheets for Enhancement of the Hydrogen Evolution Reaction

Xiao

Ruan

Bao

et al. 2020

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Plasma functionalization can increase the efficiency of MoSe2 in the hydrogen evolution reaction (HER) by providing multiple species but the interactions between the plasma and catalyst are not well understood. In this work, the effects of the ion energy and plasma density on the catalytic properties of MoSe2 nanosheets are studied. The through‐holes resulting from plasma etching and multi‐vacancies induced by plasma‐induced damage enhance the HER efficiency as exemplified by a small overpotential of 148 mV at 10 mA cm–2 and Tafel slope of 51.6 mV dec–1 after the plasma treatment using a power of 20 W. The interactions between the plasma and catalyst during etching and vacancies generation are evaluated by plasma simulation. Finite element and first‐principles density functional theory calculations are also conducted and the results are consistent with the experimental results, indicating that the improved HER catalytic activity stems from the enhanced electric field and more active sites on the catalyst, and reduced bandgap and adsorption energy arising from the etched through‐holes and vacancies, respectively. The results convey new fundamental knowledge about the plasma effects and means to enhance the efficiency of catalysts in water splitting as well insights into the design of high‐performance HER catalysts.

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Radical flux control in reactive ion beam etching (RIBE) by dual exhaust system

Kim

Jang

Kim

et al. 2022

Applied Surface Science

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Modeling of defect generation during plasma etching and its impact on electronic device performance—plasma-induced damage

Cited by 40 publications

References 113 publications

Improvement of the plasma‐induced defect generation model in Si substrates and the optimization design framework

Improvement of the plasma‐induced defect generation model in Si substrates and the optimization design framework

Effects of Ion Energy and Density on the Plasma Etching‐Induced Surface Area, Edge Electrical Field, and Multivacancies in MoSe₂ Nanosheets for Enhancement of the Hydrogen Evolution Reaction

Radical flux control in reactive ion beam etching (RIBE) by dual exhaust system

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Modeling of defect generation during plasma etching and its impact on electronic device performance—plasma-induced damage

Cited by 40 publications

References 113 publications

Improvement of the plasma‐induced defect generation model in Si substrates and the optimization design framework

Improvement of the plasma‐induced defect generation model in Si substrates and the optimization design framework

Effects of Ion Energy and Density on the Plasma Etching‐Induced Surface Area, Edge Electrical Field, and Multivacancies in MoSe2 Nanosheets for Enhancement of the Hydrogen Evolution Reaction

Radical flux control in reactive ion beam etching (RIBE) by dual exhaust system

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Product

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Effects of Ion Energy and Density on the Plasma Etching‐Induced Surface Area, Edge Electrical Field, and Multivacancies in MoSe₂ Nanosheets for Enhancement of the Hydrogen Evolution Reaction