Stochastic defect criticality prediction enabled by physical stochastic modeling and massive metrology

Wang, Chang‐An; Cao, Peigen; Delorme, Maxence; Wuu, Jen-Yi; Fu, Jiyou; Wang, Fuming; Lin, Bob C.; Zhao, Yiqiong; Peng, Yi-Hsing; Fan, Yongfa; Mu, Feng; Cheng, Bin; Wang, Jen-Shiang; Simmoms, Mark; Hunsche, S.; Patterson, Oliver D.; Pao, K.; Elmalk, Abdalmohsen; Gao, Kang; Fei, Ruochong; Zeng, Xuefeng; Zhang, Xiaolong

doi:10.1117/12.2584767

Cited by 4 publications

(5 citation statements)

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“…With the domination of stochastic variation in the EPE budget, and increasing importance of stochastic failure in yield pareto at the advanced technology nodes, it is essential to enable stochastic-aware computational lithography applications such as full chip defect failure probability prediction, stochastic-aware LMC, SMO and OPC. In 2021 SPIE, we proposed a flow to enable the full chip defect failure probability prediction with a calibrated SEPE model and statistical analysis [11]. Since then, this flow has been tested with multiple customer cases with significant defect capture rate improvement.…”

Section: Stochastic-aware Computational Lithograpy Applicationsmentioning

confidence: 99%

“…In a previously published paper in the 2021 SPIE [11], such a model was successfully used to enable full chip LMC to predict defect probability. Subsequently, it was used in multiple customer cases with significant defect capture rate improvement.…”

Section: Introductionmentioning

confidence: 99%

“…Several case studies indicated that the ILS only model failed to capture the key physical SEPE trends and pattern dependence, especially at a tight pitch region. In order to comprehend the full extent of stochastic impact, a new physics-driven stochastic model form has been proposed [11] and further improved by addressing multiple factors affecting final resist contour variability: optical sensitivity, photon and photoacid chemical kinetics (photon number fluctuation and photoacid immobility), development path uncertainty and photoresist chemistry (as shown in Fig. 3).…”

Section: Introduction To Stochastic Modelingmentioning

confidence: 99%

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A holistic approach to model-based stochastic-aware computational lithography

Wang¹,

Fan²,

Feng³

et al. 2023

Optical and EUV Nanolithography XXXVI

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With the adoption of extreme ultraviolet (EUV) lithography for high-volume production of advanced nodes, stochastic variability and resulting failures, both post litho and post etch, have drawn increasing attention. There is a strong need for accurate models for stochastic edge placement error (SEPE) with a direct link to the induced stochastic failure probability (FP). Additionally, to prevent stochastic failure from occurring on wafers, a holistic stochastic-aware computational lithography suite of products is needed, such as stochastic-aware mask source optimization (SMO), stochastic-aware optical proximity correction (OPC), stochastic-aware lithography manufacturability check (LMC), and stochastic-aware process optimization and characterization. In this paper, we will present a framework to model both SEPE and FP. This approach allows us to study the correlation between SEPE and FP systematically and paves the way to directly correlate SEPE and FP. Additionally, this paper will demonstrate that such a stochastic model can be used to optimize source and mask to significantly reduce SEPE, minimize FP, and improve stochastic-aware process window. The paper will also propose a flow to integrate the stochastic model in OPC to enhance the stochastic-aware process window and EUV manufacturability.

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Section: Stochastic-aware Computational Lithograpy Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introduction To Stochastic Modelingmentioning

confidence: 99%

See 1 more Smart Citation

A holistic approach to model-based stochastic-aware computational lithography

Wang¹,

Fan²,

Feng³

et al. 2023

Optical and EUV Nanolithography XXXVI

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show abstract

“…This is driven by the low number of photons needed to image the patterns. Recently, empirical correlations were described by De Bisschop et al [2] and in parallel compact models are being proposed to capture the stochastic nature of EUV imaging [3][4][5][6]. These stochastic models have been shown to correlate with wafer data [3,4].…”

Section: Introductionmentioning

confidence: 99%

Compact modeling of stochastics and application in OPC

Gillijns

Lee

Kim

et al. 2023

Optical and EUV Nanolithography XXXVI

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To maintain good critical dimension control, optical proximity correction (OPC) has relied on fast compact models to capture the underlying lithography process in advanced nodes. Compact models have always been deterministic in the sense that they predict the average dimensions or contours on wafer. With the introduction of extreme ultraviolet (EUV) lithography, this approach breaks down due to large variabilities in EUV lithography processes. Recently, empirical correlations were found between this variability and imaging metrics, allowing the development of compact models. Such stochastic models have been used successfully to predict hotspots. In this paper an attempt is made to apply such stochastic models during OPC to reduce the number of stochastic failures. Different OPC strategies are applied on an advanced random logic and SRAM design, focusing on a via layer with a calibrated stochastic model. Through simulations, we show that the failure rate can be reduced by using a stochastic model during OPC, at the expense of edge placement error. However, when reducing the stochastic band width to match the process variation band width, no meaningful differences were observed between process-window OPC and stochastic OPC due to uniformity of pattern dimensions in sample layout.

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“…For example, image log slope (ILS) is directly correlated to the stochastic edge placement error (SEPE) bands and the achievable local critical dimension uniformity (LCDU). Imax (peak intensity) is directly correlated to print failure rate probabilities [6][7][8][9]. Aerial image based measurements of these parameters under scanner equivalent illumination can thus provide important early input for lithographic process control [10,11].…”

Section: Introductionmentioning

confidence: 99%