BackgroundDue to the limitations of optimization degrees of freedom in traditional optical proximity correction, it cannot meet the mask optimization requirements of advanced technology nodes. Inverse lithography technology (ILT) is considered the most promising resolution enhancement technique, and the trade-off between mask optimization quality and computation time is a challenge.AimThe biggest limitation of ILT is its high computational complexity, which requires exploring an ILT algorithm that can ensure the fidelity of lithography patterns and the process variation (PV) band while also having a short computation time.ApproachWe propose UNeXt-ILT, a deep learning–based ILT technology. The UNeXt model is adopted as the backbone model, and its multi-layer perceptron structure ensures the lightweight of the model while having global context-awareness capability, thus quickly providing a high-quality initial mask and accelerating the overall computation time. In addition, the addition of mask regularization and mask filtering techniques enhances the robustness of gradient descent–based ILT algorithms and further improves the quality of mask optimization.ResultsCompared with the most advanced deep learning–based ILT algorithm, UNeXt-ILT reduces L2 error by 17.83%, reduces PV band by 8.76%, and shortens turnaround time by 34.48%.ConclusionsWe contribute to improving the robustness and computational speed of the ILT algorithm, thereby promoting its wider application.
The critical dimension scanning electron microscope (CDSEM) plays an essential role in measuring sub-nanometer scale patterns after lithography and etching process. However, its measurement capabilities are limited, making it difficult to accurately measure complex pattern such as tip-to-tip or tip to side structures. Additionally, it’s very challenging for CDSEM to perform an accurate multiple-layer multiple-process data measurement and process characterization, such as etch bias and channel length/width uniformity. This paper addresses these challenges by performing SEM contours extraction and data analysis on the gate (GT) and active area (AA) stacking structures of Static Random Access Memory (SRAM) bit cell pattern. Utilizing SIEMENS EDA's Calibre SEMSuite and Calibre OPCVerify tools, we extracted contours from SEM images to analyze process variations at the center, middle, and edge positions of the wafer. By overlaying the contours of ADI and AEI, we determined the etch bias across the entire SRAM bit cell pattern. Additionally, by overlaying the contours of AA and GT, we ascertain the channel width and length (W/L) value of the transistors. This data provides a direction for optimizing SRAM layout design and establishes a systematic method for silicon data analysis.
In IC manufacturing, there is a great demand for CD-SEM image production in the areas of computational lithography, hotspot detection, yield improvement, etc. Whereas, directing CD-SEM metrology is time-cost and skillfulness since it requires metrology engineers to manage creating CD-SEM recipes on their experience gauge by gauge, including determining suitable metrology methods, corresponding parameters, optimal Auto Focus and Addressing position based on shapes and circumstances of the metrology target. To address this, this work has proposed an efficient and labor-free way to create CD-SEM recipes via a High-Speed-Steaming (HSS) file in Recipe Director to measure lots of points within minutes automatically. For each metrology position, its numerical geometry feature obtained in Line Scan analysis achieves the ability of measurement type determining and relative parameters returning. Autofocus and Addressing position searching can be well done with sliding window patches and Fourier descriptors in the meanwhile. With an aim to facilitate massive gauge measurements and convenient usage, a Windows-platform software is also developed for engineers.
There are many kinds of OPC test pattern in Contact (CT) layer, and different measurement methods are used to measure the model data, resulting in a large simulation error of y-direction rectangle type patterns in the model. We propose a new method to build OPC model of CT layer by using SEM image contour. In this work, the SEM image contour was extracted by Calibre SEMSuite™, and the measurements of the corresponding test pattern was calculated according to the image contour. The CD-SEM (critical dimension scanning electron microscope) measurement data of anchor point was used to calibrate the modeling data and then the OPC model was built. Compared with the model tuned by CD measurements, the simulation error of y-direction rectangle type patterns caused by measurement method is effectively reduced. Our experiments show that it is feasible to build a high precision OPC model of CT layer by using this method.
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