On device EPE: minimizing overlay, pattern placement, and pitch-walk, in presence of EUV stochastics and etch variations (Conference Presentation)

Adan, Ofer; Houchens, Kevin

doi:10.1117/12.2517284

Cited by 5 publications

(3 citation statements)

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“…In first three layers, e-Beam images were grabbed using low landing energy voltage. The last two layers, where hidden structures in the stack needed to be imaged, were grabbed using high KeV utilizing the ElluminatorTM technology [3] to simultaneously acquire high-resolution images, see-through images of buried structures, and various metrics. 109 Dies were sampled in two locations-total 218 locations per wafer and per layer.…”

Section: E-beam Imaging and Image Grabbingmentioning

confidence: 99%

Direct yield prediction from SEM images

Choona

Linshiz

Pres

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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In line Electrical measurement (E-Test) are the most effective predictors for EOL yield control. As technology progress with scaling, the number. of process layers increases, allowing in-line electrical measurements only after several months since lot started process in-line. As a result, each E-Test monitor controls longer and more challenging process loop. Most of the in-line pattern control that impact electrical performance measured separately for each pattern polygon and material properties. In addition, Edge Placement Error (EPE) methodology, allows combination of multiple dimensions like CD, Overlay and LER measurements to better predict yield impact. Technology shrinkage, resulting that transistor electrical performance, defined by more geomaterial parameters as well as material compositions and defectivity. In this paper we demonstrate a direct prediction from high resolution Scanning Electron Microscope (SEM) images to the first inline electrical measurement (M1) using Deep Learning (DL) techniques. The DL model provide early prediction of electrical performance, describing accurately Within Wafer (WIW) variation weeks earlier than the actual electrical measurements. Multiple layers prediction may indicate suspected process loop that modulate majority of variation and save time to solution. It can be achieved since the DL model utilizes complementary information exist on the full e-Beam image like materials and defectivity. The following results will indicate that accumulating information collected from several layers will improve prediction sensitivity and lead to even more accurate prediction capabilities. We assume that the effectiveness of the proposed prediction method will increase with process complexity, since the modulation of the existing yield predictors is losing sensitivity as design rule shrinks. In addition, since fabrication phase gets longer, the time to actual electrical measurements increase, making an early, nondestructive, and accurate prediction for electrical performance more and more valuable.

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Section: E-beam Imaging and Image Grabbingmentioning

confidence: 99%

Direct yield prediction from SEM images

Choona

Linshiz

Pres

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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“…For the better proxy to the yield or patterning excursion of the device, Edge Placement Error (EPE) was revisited and clarified as yield limiting metric [1], [5]- [8] . The metric is based on large statistics that combines all the variations both on CD and overlay domains: systematic, global, and local variations [6] .…”

Section: Introductionmentioning

confidence: 99%

Real time EPE measurement as a yield correlated metrology on advanced DRAM nodes

Jee,

You,

Lee

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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The Edge Placement Error (EPE) is growing concerns due to the complexity increases of process variation as the design rule shrinkage of DRAM device. The EPE is a well-accepted metric which can be derived from CD, Overlay and LER measurements from more than patterning layers that concerned [1] . Therefore, real time EPE measurement becomes a major factor to monitor and control the pattern fidelity.The pattern fidelity could be found from the edge placement measurement as a distance to design intent as possible without pattern defects. However, the traditional application of photolithography and etch biases according to a design rule or model for identifying pattern fidelity has inherent low TMU, multiple non consistence data sources and time-consuming off-line analysis.In previous works [2]-[4] , we demonstrated the innovative e-Beam EPE metrology application using All-In-One (AIO) methodology to comply the required Total Measurement Uncertainty (TMU) and Time to Result (TTR) on the advanced DRAM nodes. AIO imaging and analysis methodology that deconvolute CD, overlay and relevant EPE metrics from a single see-through image is the most important differentiation for this EPE analysis approach. The in-cell direct EPE measurement with All-In-One (AIO) imaging and massive sampling demonstrates the better process controls and monitoring from the co-optimization of multiple control parameters and direct measurement of the yield relevant metrics.In this paper, we would like to show a couple of EPE monitoring use cases which shows good correlation to the final yield map through the massive and multi-layer measurements. Especially, it is expected that the EPE component which measures the edge-to-edge distance between different features of multi-layers can be a useful indicator for predicting yield along with CD and overlay. To investigate the local and random variabilities, which local stochastic effects are contained, we also studied the degree of yield prediction of the EPE component with increasing number of measurement sites in local area. It is proposed that using a large amount of measurement sites allows to improve the yield prediction accuracy to a certain extent, which means the local stochastic effects can be effectively analyzed with the use of massive metrology approach. In addition, from the prediction accuracy study using EPE model-based machine learning, we proved that the EPE is sufficiently sensitive indicator to capture potential yield-loss problems in normal wafer, as well. Therefore, in-line EPE monitoring using AIO metrology enables the root-cause analysis of patterning weak points and provides a better process monitoring/correction solution to enable faster advanced DRAM node development ramp and high-volume stability.

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“…Photolithographic technology is a major method to produce nanochips in node size down to 7 nm or less at present. − For this purpose, a fabrication facility operates extreme-ultraviolet (EUV) light at 13.5 nm from the incoherent tin-droplet source. − A commercial EUV scanner currently delivers the power at 13.5 nm above 100 W, with targets to 500 W in the near future; under such great operating power, the heat loads of elements in the machine are invariably a matter of concern and require monitoring.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Temperature of a Mo/Si Mirror with Photoluminescence in Extreme-Ultraviolet Lithography

Lo¹,

Peng²,

Lu³

et al. 2022

ACS Appl. Electron. Mater.

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Extreme-ultraviolet lithography uses a Mo/Si multilayer mirror as an optical unit to produce nanochips in a semiconductor fabrication plant. The Mo/Si multilayer mirror radiates an intense photoluminescence in the wavelength region 200–420 nm upon excitation with light at 13.5 nm. The emissions of the Mo/Si multilayer mirror depend noticeably on its operational temperature; we thus suggest to apply this property to directly monitor the temperature of a Mo/Si multilayer mirror in extreme-ultraviolet lithography. This scheme to monitor the temperature of a mirror can trace the real-time temperature due to the intrinsic property of photoluminescence.

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On device EPE: minimizing overlay, pattern placement, and pitch-walk, in presence of EUV stochastics and etch variations (Conference Presentation)

Cited by 5 publications

References 0 publications

Direct yield prediction from SEM images

Direct yield prediction from SEM images

Real time EPE measurement as a yield correlated metrology on advanced DRAM nodes

Monitoring the Temperature of a Mo/Si Mirror with Photoluminescence in Extreme-Ultraviolet Lithography

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