Deficits in Processing Case Markers in Individuals with Aphasia

Eun, Sung Jee; Hyun, Jeong Kwi; Reum, Sim Yeo; Eun, Lee Soo; Ok, Mo Kyeong

doi:10.3389/conf.fpsyg.2015.65.00045

Cited by 2 publications

(1 citation statement)

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“…The Variable Threshold Resist Model (VTRM) and CM1 model are numerical fitting models using the aerial image as the input to predict the contour of ADI data [1][2][3] . The image parameter space (IPS) is an important metric characterizing the lithographic properties of a specific layout [4][5][6][7] . In the model calibration, image parameters play a key role.…”

Section: Introductionmentioning

confidence: 99%

Test pattern generation by conditional generative model labeled by image parameters

Qi,

Xu,

et al. 2024

DTCO and Computational Patterning III

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Test pattern generation plays a vital role in the model calibration in the optical proximity correction process. Traditional OPC resist models mainly use the image parameters such as the minimum intensity, the maximum intensity, the slope of intensity along the cut lines crossing the gauge points as their input parameters to calculate the resist contour position. To guarantee the accuracy of the resist model over the whole design layout, it is important that the image parameter space of the test patterns used to calibrate the OPC model covers the image parameter space of the original design layout. We present a method to generate test patterns based on the provided image parameters. The method is based on the adversarial neural network. With this method, we can prepare the test patterns with the desired image parameter coverage.

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