Siemens Industry Software Gmbh

Mentor Graphics Corporation

Germain Louis Fenger

Azat M Latypov

John L Sturtevant

Kiarash Ahi

Christopher Clifford

Andrew Burbine

Gurdaman Khaira

Hsin-Wei Wu

Yuri Granik

Konstantinos G Adam

Sandip Halder

Ananthan Raghunathan

Shumay Dou Shang

Michael Christopher Lam

Bappaditya Dey

Mark Pereira

Hyejin Jin

Bhamidipati Venkata Rama Samir

This application discloses a computing system to obtain a wafer image of an electronic device having physical structures manufactured using one or more lithographic masks associated with a layout design describing the electronic design. The computing system can implement an unsupervised deep learning algorithm to process the wafer image to remove at least some noise from the wafer image, which generates a denoised wafer image. The computing system can extract contours corresponding to the physical structures of the electronic device from the denoised wafer image of the electronic device without use of the layout design or a mask design. The computing system can calibrate the layout design or the mask design describing the one or more lithographic masks based, at least in part, on the contours extracted from the denoised wafer image.

Wafer image denoising and contour extraction for manufacturing process calibration

A computing system implementing a raw data filtering tool can aggregate multiple wafer images depicting a portion of an electronic device into a reference image, detect one or more of the wafer images have defects based on a comparison of the reference image to the wafer images, and generate a gauge file to include a set of the wafer images selected based on the detection of defects in the wafer images. The raw data filtering tool also can iteratively build defect maps that include differences between the reference image and the wafer images, and characterize the detected defect in the wafer images with a size and a location based on the defect maps. The raw data filtering tool can provide feedback to a foundry about wafer images were excluded from the set of the wafer images based on the detection of defects in the wafer images.

Wafer image defect detection and characterization for manufacturing process calibration

Systems and methods for calculating a printed area metric indicative of stochastic variations of the lithographic process are disclosed. Lithography is a process that uses light to transfer a geometric pattern from a photomask, based on a layout design, to a resist on a substrate. The lithographic process is subject to random stochastic phenomena, with the resulting stochastic randomness potentially becoming a major challenge. To characterize the stochastic phenomena, a printed area metric may be generated analytically (rather than via simulations) and comprise one or more defined moments for a printed area distribution associated with the printed area that are indicative of one or more aspects associated with printing. For example, the printed area metric may be indicative of the likelihood of printing within the printed area or the variance of printing within the printed area due to stochastic randomness in one or both of exposure or resist process.

Method and system for calculating printed area metric indicative of stochastic variations of the lithographic process

A computing system implementing an optical proximity correction model verification tool can determine parameters for design patterns associated with an integrated circuit described in a layer file, and determine differences between the design patterns and calibration patterns utilized to calibrate an optical proximity correction (OPC) model configured to predict a printed image on a substrate corresponding to a layout design for the integrated circuit by determining distances between the determined parameters for the design patterns and parameters for the calibration patterns. The computing system can classify the design patterns with a modeling capability of the OPC model for the design patterns based on the differences between design patterns and the calibration patterns and possibly error rates of the OPC model associated with the calibration patterns or lithographic difficulty of the calibration patterns. The computing system can modify the layer file to include the classifications of the design patterns.

Optical proximity correction model verification

A method and system for calculating probability of success or failure for a lithographic process due to stochastic variations of the lithographic process are disclosed. Lithography is a process that uses light to transfer a geometric pattern from a photomask, based on a layout design, to a resist on a substrate. The lithographic process is subject to random stochastic phenomena, such as photon shot noise and stochastic phenomena in the resist process and resist development, with the resulting stochastic randomness potentially becoming a major challenge. The stochastic phenomena are modeled using a stochastic model, such as a random field model, that models stochastic randomness the exposure and resist process. The stochastic model inputs light exposure and resist parameters and definitions of success of success or failure as to the lithographic process, and outputs a probability distribution function of deprotection concentration indicative of success or failure probability of the lithographic process. In turn, the probability distribution function may be used to modify one or both of the light exposure and resist parameters in order to reduce the effect of stochastic randomness on the lithographic process.

Method and system for calculating probability of success or failure for a lithographic process due to stochastic variations of the lithographic process

This application discloses a computing system implementing an optical proximity correction model calibration tool to determine parameters for gauges describing features of an integrated circuit. The gauges include values corresponding to measurements collected for a set of the features. The optical proximity correction model calibration tool can ascertain densities of the gauges based on the measurements associated with the parameters for the gauges, and set weights for the gauges based, at least in part, on the densities. The optical proximity correction model calibration tool can calibrate an optical proximity correction (OPC) model using the weights for the gauges. The OPC model calibrated with the weights of the gauges can be utilized to predict of a printed image on a substrate described by a mask layout design corresponding to the integrated circuit.

Optical proximity correction modeling with density-based gauge weighting

Disclosed are techniques for processing layout designs based on dynamically-generated lithographic models. Lithographic models are determined for a plurality of regions of a reticle prior to lithographic simulation. During lithographic simulation, lithographic models for a small area within a particular region are generated based on the lithographic models for the particular region, the lithographic models for one or more neighboring regions, and location information of the small area relative to the region and to the one or more neighboring regions. The lithography models comprise illuminating and imaging system models and mask electro-magnetic field models.

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Gladstone, OR, United States of America

Germain Louis Fenger