Date of download: 6/2/2016 Copyright © 2016 SPIE. All rights reserved. Metrology quality and capability association to the profitability. Figure Legend: From: Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes J. Micro/Nanolith. MEMS MOEMS. 2014;13(4): doi: /1.JMM

Date of download: 6/2/2016 Copyright © 2016 SPIE. All rights reserved. With advances in IC technology tighter CD control is needed posing tighter metrology uncertainty requirements of few Angstroms as evident from the projections in the International technology roadmap for semiconductors (ITRS), Figure Legend: From: Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes J. Micro/Nanolith. MEMS MOEMS. 2014;13(4): doi: /1.JMM

Date of download: 6/2/2016 Copyright © 2016 SPIE. All rights reserved. Examples of inherent capability limitations of some metrology techniques. Figure Legend: From: Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes J. Micro/Nanolith. MEMS MOEMS. 2014;13(4): doi: /1.JMM

Date of download: 6/2/2016 Copyright © 2016 SPIE. All rights reserved. Pathway from metrology capability and quality limited regime to the accurate prediction regime. Figure Legend: From: Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes J. Micro/Nanolith. MEMS MOEMS. 2014;13(4): doi: /1.JMM

Date of download: 6/2/2016 Copyright © 2016 SPIE. All rights reserved. (a) Schematic of process steps 1 and 2. (b) MBIR model 2 optimization. (3) MBIR models 1 and 2 matching with AFM reference data. Figure Legend: From: Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes J. Micro/Nanolith. MEMS MOEMS. 2014;13(4): doi: /1.JMM

Date of download: 6/2/2016 Copyright © 2016 SPIE. All rights reserved. Accurate critical dimension (CD) metrology challenge for sub-40 nm trenches in resists. (a) CD-AFM image of 80/80 and 40/40 patterns in 193-nm resist. (b) CD-AFM image of variable line/space CDs in extreme ultraviolet (EUV) resists. (c) Simple extrapolation based on few data points (three rightmost data) can result in more than 2 nm inaccuracy compared to actual CD-AFM measurement based on 80/80 pattern. Figure Legend: From: Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes J. Micro/Nanolith. MEMS MOEMS. 2014;13(4): doi: /1.JMM

Date of download: 6/2/2016 Copyright © 2016 SPIE. All rights reserved. CD-SEM resist shrinkage calibration based on the postshrink (after 12 consecutive CD-SEM measurements) CD-AFM measurements and verification. The AFM image in the plot shows the pre- (left half) and post- (right half) SEM exposed regions. The table shows the total measurement uncertainty (TMU) and the average offset between CD-AFM and extrapolated CDs for dose and focus variation on the wafer for 80/80 pattern. Figure Legend: From: Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes J. Micro/Nanolith. MEMS MOEMS. 2014;13(4): doi: /1.JMM

Date of download: 6/2/2016 Copyright © 2016 SPIE. All rights reserved. TMU analysis of extrapolated CD and the DT-AFM CD for 40/40 pattern. Figure Legend: From: Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes J. Micro/Nanolith. MEMS MOEMS. 2014;13(4): doi: /1.JMM

Date of download: 6/2/2016 Copyright © 2016 SPIE. All rights reserved. General schematic of an artificial neural network (ANN) showing three main layers: input, hidden, and output. Figure Legend: From: Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes J. Micro/Nanolith. MEMS MOEMS. 2014;13(4): doi: /1.JMM

Date of download: 6/2/2016 Copyright © 2016 SPIE. All rights reserved. EUV resist pattern with varying line/trench CD imaged with CD-AFM. The wafer map shows that the dose variation is along the row and data have been collected along the row with AFM, CD-SEM, and CD-AFM again. The plot shows the correlation between the ANN output and the CD-AFM measurement for the smallest line used for prediction. The table at the bottom lists the TMU analysis statistics. Figure Legend: From: Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes J. Micro/Nanolith. MEMS MOEMS. 2014;13(4): doi: /1.JMM

Date of download: 6/2/2016 Copyright © 2016 SPIE. All rights reserved. Optimization of five scatterometry litho models (80/80 pattern) using different sets of reference data and TMU of the outputs from these models regressed against the CD-AFM data. Figure Legend: From: Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes J. Micro/Nanolith. MEMS MOEMS. 2014;13(4): doi: /1.JMM

Date of download: 6/2/2016 Copyright © 2016 SPIE. All rights reserved. Performance of five litho scatterometry models derived from 80/80 pattern by changing geometry to 40/40 and judged by the TMU of model output and CD-SEM mid-CD reference data. Figure Legend: From: Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes J. Micro/Nanolith. MEMS MOEMS. 2014;13(4): doi: /1.JMM