Improving OCD time to solution using Signal Response Metrology

Fang, Fang; Zhang, Xiaoxiao; Vaid, Alok; Pandev, Stilian; Sanko, Dimitry; Ramanathan, Vidya; Venkataraman, Kartik; Haupt, Ronny

doi:10.1117/12.2219775

Cited by 8 publications

(5 citation statements)

References 1 publication

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“…In this work, the recipe was developed by the combined spectroscopic Mueller matrix measured using the SpectraShape dimensional metrology system and a physics-based machine learning algorithm [7,8]. The workflow of the recipe development procedure is sketched in Figure 6.…”

Section: Wafer Preparation and Recipe Developmentmentioning

confidence: 99%

“…This work uses a physics-based machine learning algorithm for in-die overlay recipe development. Both real spectra collected by the SpectraShape and theoretical spectra generated from the scatterometry model are trained against their corresponding reference and synthetic reference data respectively to predict the overlay value [7,8]. Spectra from the training set were kept as a reference and compared with spectra under the testing set.…”

Section: Introductionmentioning

confidence: 99%

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Scatterometry and machine learning for in-die overlay solution

Yeh¹,

Chouaib²

2023

Metrology, Inspection, and Process Control XXXVII

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Advanced technology nodes require tighter lithography overlay specifications with higher throughput and lower cost of ownership. Today, with the accelerating complexity of nanoelectronics for memory applications, an increased emphasis is placed on controlling the on-product overlay (OPO) budget. Consequently, accurate in-die overlay measurements play a critical role after the etching process (ACI) for which it can better reflect the actual product overlay. Here we propose a solution with the combined spectroscopic full Mueller matrix, measured with the KLA next-generation SpectraShape™ dimensional metrology system and a physics-based machine learning algorithm. Both real spectra collected by the SpectraShape and theoretical spectra generated from the scatterometry model are trained against their corresponding ground truth reference and synthetic reference data respectively to predict overlay. Theoretical and experimental results show that the Mueller elements are sensitive to very small changes in the overlay parameters which can enable inline, high-throughput overlay metrology. Accuracy, robustness, and precision on massive datasets using design of experiments (DOE) wafers are presented and discussed. Moreover, the measurement reliability is assessed with a key performance indicator (KPI), designed to flag a process excursion in a high-volume manufacturing (HVM) environment. Good agreement is observed between the KPI and the actual model accuracy.

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Section: Wafer Preparation and Recipe Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scatterometry and machine learning for in-die overlay solution

Yeh¹,

Chouaib²

2023

Metrology, Inspection, and Process Control XXXVII

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“…A. Related work a) Metrology: In [5], [6], the authors develop a method for Optical CD (OCD) metrology that is related to the standard scatterometry approach. They apply machine learning techniques in order to avoid the model development effort and approximations associated with standard OCD.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning on Multiplexed Optical Metrology Pattern Shift Response Targets to Predict Electrical Properties

Ashby,

Truffert,

Cerbu

et al. 2024

IEEE Trans. Semicond. Manufact.

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Doing high throughput high accuracy metrology in small geometries is challenging. One approach is to build easily measurable proxy targets onto dies and make a predictive model based on those signals. We use optical Pattern Shift Response (PSR) proxy targets to build predictive models of the electrical characteristics of devices in the Back End Of Line (BEOL). Given the wide choice of PSR targets, we explore how to select combinations of them to maximise the utility of the features for building an accurate Machine Learning (ML) model; we call this approach Multiplexed Optical Metrology. We also explore the trade-off between chip area dedicated to targets and achievable accuracy. We run ML experiments using different selections of targets measured at different stages of BEOL processing: postlithography and post-Chemical-Mechanical-Planarisation (CMP). Our results show that a) reasonable predictive performance can be achieved for a reasonable area budget; b) ML model performance across target families varies significantly, thus justifying the need for careful selection of targets; c) longitudinal measurements of targets increases accuracy for no extra area penalty; d) increasing the the number of targets gives some improvement in accuracy for a dataset of this size, but relatively small compared to the increase in area budget needed.Ultimately we aim to do die-level yield prediction using these techniques. We discuss how collecting a larger dataset with appropriate yield information is the logical next step to achieving this.

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“…[8][9]. This work introduces a physical-based machine-learning algorithm [10][11][12] recipe that is capable of in-die overlay measurement by training both real spectra collected from SpectraShape 11k and theoretical spectra generated from the scatterometry model against the corresponding reference to predict the overlay value. Spectra from the training set was kept as a reference and compared with spectra under the testing set.…”

Section: Introductionmentioning

confidence: 99%

Optical in die NZO benefit for DRAM manufacturing

Yeh,

Tsai,

Chu

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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Today, with the accelerating complexity of nanoelectronics for memory applications, in-die overlay metrology has required much tighter control. A typical in-device overlay control strategy utilizes high-voltage SEM metrology across several key layers, but lot and wafer sampling is limited due to low system throughput. Our objective is to find a faster, more robust, and more efficient optical metrology solution that can produce the same in-die overlay results vs. SEM. In this work, we create a novel solution using the KLA SpectraShape™ 11k dimensional metrology system to demonstrate improved nonzero overlay (NZO) control that meets the tighter overlay budget requirement.We combined the spectroscopic Mueller matrix of SpectraShape 11k and the machine learning algorithm of TurboShape™ modeling software. Both real spectra collected by SpectraShape 11k and theoretical spectra generated from the scatterometry model are trained against their corresponding SEM reference and synthetic reference data respectively to predict the overlay value. Accurate and robust optical in-device overlay results are proven with a high correlation to the HV-SEM data. In addition, the SpectraShape 11k in-device overlay is equipped with a few key performance indicators (KPIs) including CIndex™ and CD profile, which are designed to flag process excursions in an HVM environment. Good agreement is observed between the KPIs and overlay delta to HV-SEM. Finally, the 4x-8x throughput advantage of optical metrology in-device overlay vs. SEM in-device overlay allows users to set more dense wafer measurements by lot or dense site measurements by wafer, enabling better lot-to-lot or wafer-to-wafer NZO control.

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Improving OCD time to solution using Signal Response Metrology

Cited by 8 publications

References 1 publication

Scatterometry and machine learning for in-die overlay solution

Scatterometry and machine learning for in-die overlay solution

Machine Learning on Multiplexed Optical Metrology Pattern Shift Response Targets to Predict Electrical Properties

Optical in die NZO benefit for DRAM manufacturing

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