Model-based verification for first time right manufacturing

Bruce, James A.; Conrad, Edward W.; Dick, Gregory J.; Nickel, D. John; Smolinski, Jacek G.

doi:10.1117/12.600552

Cited by 12 publications

(16 citation statements)

References 2 publications

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“…The figures on the top show the original target and the corresponding process variability bands (PV-bands) as obtained by performing multiple aerial image simulations across the expected range of dose and defocus values 11,12 . For our experiments we used a dose range of +/-3% and a defocus range of +/-100nm.…”

Section: Resultsmentioning

confidence: 99%

“…From the mask solution from each flow we ran lithography simulation using Calibre OPCVerify at different dose and focus corners. We used dose settings of +/-3% and defocus of +/-100nm and compiled the lithographic contours into a process variability band (PV-band) 11,12 . The area of the PV band is a measure of robustness of the layout to process variations.…”

Section: Resultsmentioning

confidence: 99%

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Integrated model-based retargeting and optical proximity correction

Agarwal¹,

Banerjee²

2011

SPIE Proceedings

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Conventional resolution enhancement techniques (RET) are becoming increasingly inadequate at addressing the challenges of subwavelength lithography. In particular, features show high sensitivity to process variation in low-k1 lithography. Process variation aware RETs such as process-window OPC are becoming increasingly important to guarantee high lithographic yield, but such techniques suffer from high runtime impact. An alternative to PWOPC is to perform retargeting, which is a rule-assisted modification of target layout shapes to improve their process window. However, rule-based retargeting is not a scalable technique since rules cannot cover the entire search space of two-dimensional shape configurations, especially with technology scaling. In this paper, we propose to integrate the processes of retargeting and optical proximity correction (OPC). We utilize the normalized image log slope (NILS) metric, which is available at no extra computational cost during OPC. We use NILS to guide dynamic target modification between iterations of OPC. We utilize the NILS tagging capabilities of Calibre TCL scripting to identify fragments with low NILS. We then perform NILS binning to assign different magnitude of retargeting to different NILS bins. NILS is determined both for width, to identify regions of pinching, and space, to locate regions of potential bridging. We develop an integrated flow for 1x metal lines (M1) which exhibits lesser lithographic hotspots compared to a flow with just OPC and no retargeting. We also observe cases where hotspots that existed in the rule-based retargeting flow are fixed using our methodology. We finally also demonstrate that such a retargeting methodology does not significantly alter design properties by electrically simulating a latch layout before and after retargeting. We observe less than 1% impact on latch Clk-Q and D-Q delays post-retargeting, which makes this methodology an attractive one for use in improving shape process windows without perturbing designed values.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Integrated model-based retargeting and optical proximity correction

Agarwal¹,

Banerjee²

2011

SPIE Proceedings

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“…5, were used for our analysis. Each sensing element was chosen to detect a different type of functional failure: (1) serpentine to detect open and (2) comb structures to detect bridging problems. Using such test structures, measurements are performed to assess the presence of the defects, which can be inferred by the presence of short circuits or open circuits using simple resistance measurements.…”

Section: Electrically Testable Structures and Methods Formentioning

confidence: 99%

“…Hence, with the current lithographic technology the requirement for post-OPC verification has become critical. 1,2 The purpose of OPC verification is to test whether OPC sufficiently compensates for process transformations and to ensure an adequate patterning margin to meet production requirements. It provides a thorough check of lithographic patterning failures that can affect yields, such as bridging and pinching.…”

Section: Introductionmentioning

confidence: 99%

Electrical validation of through-process optical proximity correction verification limits

Jaiswal¹,

Kuncha²,

Bharat³

et al. 2010

J. Micro/Nanolith. MEMS MOEMS

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Electrical validation of through process optical proximity correction verification limits in 32-nm process technology is presented. Correlation plots comparing electrical and optical simulations are generated by weighting the probability of occurrence of each process conditions. The design of electrical layouts is extended to subdesign rules to force failure and derive better correlation between electrical and simulated outputs. Some of these subdesign rule designs amplify the failures induced by an exposure tool, such as optical aberrations. Observations in this regard are reported. Sensitivity with respect to dimensions, orientations, and wafer distribution are discussed in detail. C 2010 Society of Photo-Optical Instrumentation Engineers.

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“…We account for variability in dose and focus [7,8] by modeling the resist image at different process conditions. The process variability band is the most commonly used metric in the industry [9,10], which represents the variability in the position of the contour. It is simply the XOR of multiple resist images.…”

Section: Process Variability Band Generationmentioning

confidence: 99%

ICCAD-2013 CAD contest in mask optimization and benchmark suite

Banerjee¹,

Li²,

Nassif³

2013

2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

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Optical microlithography is the technique of printing a set of shapes on a wafer using light transmitted through a template called a mask. Repeatedly printing and stacking such shapes on top of each other to build electrical circuits allows us to manufacture chips in high volume. However this technique has now reached its fundamental physical limits of resolution. Current 193nm wavelength light is no longer sufficient to reliably transfer patterns which are now in the sub-100nm dimensional range. This has led to increased research in optimizing lithographic masks to pre-compensate for distortions introduced by the lithographic process. This is called mask optimization. In this contest, students are provided with a sample lithographic model which simulates the transfer of a mask pattern on to wafer. The mask is assumed to be a pixelated template, where every pixel can be turned on or off, to indicate where light passes through, or is blocked. Contestants are also provided with models to predict the robustness of their pattern i.e. how much variability is in the transferred pattern. Given these tools, the objective is to minimize the variability in the wafer image, as measured by process variability (PV) bands. This is subject to the constraints of runtime and satisfying pattern fidelity i.e. the transferred pattern should resemble the target pattern. Benchmarks are provided in the form of collections of geometric shapes, each of which provides a challenge in printing at sub-wavelength.

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Model-based verification for first time right manufacturing

Cited by 12 publications

References 2 publications

Integrated model-based retargeting and optical proximity correction

Integrated model-based retargeting and optical proximity correction

Electrical validation of through-process optical proximity correction verification limits

ICCAD-2013 CAD contest in mask optimization and benchmark suite

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