Advanced in-line metrology strategy for self-aligned quadruple patterning

Chao, Robin; Breton, Mary; Lherron, B.; Mendoza, Brock; Muthinti, Raja; Nelson, Florence; Pena, Abraham De La; Le, Fee li; Miller, Eric R.; Sieg, Stuart; Demarest, J.; Gin, Peter; Wormington, Matthew; Cepler, Aron; Bozdog, Cornel; Sendelbach, Matthew; Wolfling, Shay; Cardinal, Tom; Kanakasabapathy, Sivananda; Gaudiello, John G.; Felix, Nelson

doi:10.1117/12.2220601

Cited by 4 publications

(8 citation statements)

References 4 publications

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“…OCD measurements were obtained at five discrete process steps in the SAQP process (as described in Sec. 3) using the methodology described by Chao et al 6 . The OCD dataset of ∼30 wafers was collected from a stable process route under active process control without any experimental splits.…”

Section: Methodsmentioning

confidence: 99%

“…Indeed, SAQP is actively employed in semiconductor manufacturing of FinFET devices. 5,6 The SAQP process employs multiple nonlithographic sidewall spacer image transfers to reduce the pitch to a quarter of the original lithographic pitch. Errors in earlier process steps can propagate through the subsequent deposition and etch steps resulting in unwanted variations in the final structure created with SAQP.…”

Section: Introductionmentioning

confidence: 99%

“… – 5 While it can be used to pattern many line-space layers, this paper will describe the use of SAQP for the fin layer. Indeed, SAQP is actively employed in semiconductor manufacturing of FinFET devices 5 , 6 …”

Section: Introductionmentioning

confidence: 99%

“…Chao et al 6 . used optical critical dimension (OCD) data to create a calibrated SAQP measurement model, using a data feedforward approach and verification by reference metrology.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning

Halle¹,

Dunn²,

Gabor³

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

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Background: Predictive estimates of the final process outcome(s) of multistep, coupled processes can be difficult to make based on data measured at the various process steps. Self-aligned quadruple patterning (SAQP) is an example of such a process where the prediction of pitch-walk is desired at the various process steps.Aims: Be able to both predict pitch-walk values and the uncertainty in the predicted values at SAQP process steps based on optical critical dimension (OCD) spectroscopy outputs (dimensions, angles, thicknesses, and so on) of mandrel, spacer, and other SAQP features.Approach: Train a neural network using OCD-modeled values of an SAQP process to be able to predict SAQP pitch-walk at early process steps. Use Bayesian dropout approximation (BDA), a methodology using Bayesian inference with stochastic neural networks, to estimate uncertainty in the predicted SAQP pitch-walk.Results: Able to predict pitch-walk values, and the uncertainty in the predictions, of the final SAQP structure after the deposition of the first spacer. The pitch-walk predictions become more accurate as OCD information from the bottom mandrel RIE and bottom spacer are added as inputs to the BDA network. Conclusions:In contrast to a single output value that traditional neural networks would predict, BDA makes an estimated distribution of predictions, where the BDA network gives both a most likely value as well as a distribution of potential values. While this paper shows the power of BDA to predict SAQP pitch walk, it is expected that BDA will be a valuable tool to analyze many data sets in semiconductor manufacturing to help improve yield and performance.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Chao et al 6 . used optical critical dimension (OCD) data to create a calibrated SAQP measurement model, using a data feedforward approach and verification by reference metrology.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning

Halle¹,

Dunn²,

Gabor³

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

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“…An automated analysis package performs peak-based analysis fitting the intensity distribution in RSMs to determine the composition, relaxation and tilt in epitaxial materials. Additionally, modelling software has been extended to simulate RSMs from patterned structures and can automatically extract and fit line profiles from the RSMs for thickness and other measurements, such as pitch and pitch-walking analysis [12]. An example of a simulated 113GE RSM of a strained SiGe layer grown on SiGe SRB is shown in Figure 10.…”

Section: Automated Rsm Analysismentioning

confidence: 99%

Inline monitoring of SiGe strain relaxed buffers (SRBs) using high-resolution X-ray diffraction: AM: Advanced metrology

Mendoza¹,

Lherron²,

Loubet³

et al. 2016

2016 27th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)

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We describe the use of high resolution X-ray diffraction (HRXRD) for inline metrology of strain relaxed buffer (SRB) layers and epitaxial layers grown thereon. The use of SRBs as a virtual substrate is a promising candidate for advanced CMOS logic at the 7 nm technology node and presents some unique challenges to traditional HRXRD measurements. To overcome these challenges, reciprocal space maps (RSMs) were employed to characterize different films on SRBs. We discuss the measurement strategies and recent improvements to X-ray metrology tools that enable these measurements for inline process control. Furthermore, advances in the automated data extraction and analysis are introduced.

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Design technology co-optimization assessment for directed self-assembly-based lithography: design for directed self-assembly or directed self-assembly for design?

Lai

Liu

Tsai

et al. 2017

J. Micro/Nanolith. MEMS MOEMS

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Advanced in-line metrology strategy for self-aligned quadruple patterning

Cited by 4 publications

References 4 publications

Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning

Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning

Inline monitoring of SiGe strain relaxed buffers (SRBs) using high-resolution X-ray diffraction: AM: Advanced metrology

Design technology co-optimization assessment for directed self-assembly-based lithography: design for directed self-assembly or directed self-assembly for design?

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