Gauda, Inc.

Canesta, Inc.

D2s, Inc.

Guada, Inc.

Ilhami H Torunoglu

Ahmet Karakas

P Jeffrey Ungar

Erich Elsen

Patrick O'Connor

Rajeev Nagabhirana

Cyrus Soli Bamji

Abbas Rafii

Salih Burak Gokturk

Helena Roeber

A system and method for mask data preparation (MDP) uses pixel processing algorithms running on parallel processing platforms such as central processing units (CPUs) and graphical processing units (GPUs). Proximity effects correlation, dose, and bias corrections are performed on a pixel basis. In some embodiments, striping of a decorated database in parallel using multiple graphic processors is performed. While performing a first light path simulation for a first stripe for a mask, a second light path simulation is performed for a second stripe for the mask. Using a result of the striping and first and second light path simulations, dose adjustment during a mask processing on a pixel of the mask is performed.

System and method for data path handling, shot count minimization, and proximity effects correction related to mask writing process

Minimizing memory access by converting a given matrix computation into a set of low-order polynomials. The set of polynomials is processed using parallel computational hardware such as graphical processing units.

Approximate calculation of 2D matrix entries via GPU

Optical proximity correction techniques performed on one or more graphics processors improve the masks used for the printing of microelectronic circuit designs. Execution of OPC techniques on hardware or software platforms utilizing graphics processing units. GPUs may share the computation load with the system CPUs to efficiently and effectively execute the OPC method steps.

Performing OPC on hardware or software platforms with GPU

Computationally intensive electronic design automation operations are accelerated with algorithms utilizing one or more graphics processing units. The optical proximity correction (OPC) process calculates, improves, and optimizes one or more features on an exposure mask (used in semiconductor or other processing) so that a resulting structure realized on an integrated circuit or chip meets desired design and performance requirements. When a chip has billions of transistors or more, each with many fine structures, the computational requirements for OPC can be very large. This processing can be accelerated using one or more graphics processing units.

Techniques of optical proximity correction using GPU

In an electronic design automation technique for optical proximity correction, a mask is represented by a function with an exact analytical form over a mask region. Using the physics of optical projection, a solution based on a spatial frequency analysis is determined. Spatial frequencies above a cutoff are determined by the optical system do not contribute to the projected image. Spatial frequencies below this cutoff affect the print (and the mask), while those above the cutoff only affect the mask. Frequency components in the function below this cutoff frequency may be removed, which will help to reduce computational complexity.

Lithography mask functional optimization and spatial frequency analysis

In an electronic design automation technique for optical proximity correction, an optimized mask function that has values other than those allowed for a particular mask type, such as 0 and 1 for a chrome-on-glass binary mask, evolves it to a solution restricted to these values or narrow intervals near them. The technique 'regularizes' the solution by mixing in a new cost functional that encourages the mask to assume the desired values. The mixing in may be done over one or more steps or even 'quasistatically,' in which the total cost functional and the mask is brought from pure goodness-of-fit to the printed layout for given conditions to pure manufacturability by keeping the total cost functional minimized step-by-step. A goal of this gradual mixing-in is to do thermodynamically optimal work on the mask function to bring it to manufacturable values.

Regularization method for quantizing lithography masks

Minimizing memory access by converting a given matrix computation into a set of low-order polynomials. The low-order polynomials can be used by dividing the domain of the polynomials into smaller subregions. If the domain is divided into equal intervals, the low-order polynomial can be used to approximate results from the matrix computation. The set of polynomials is processed using parallel computational hardware such as graphical processing units.

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Monte Sereno, CA, United States of America

Ilhami H Torunoglu