Approximate calculation of 2D matrix entries via GPU
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.
1. A system comprising:
a computing system comprising at least one central processing unit and at least one graphics processing unit;
a user interface for interacting with the computer system;
a computer readable medium comprising data describing size and placement of features to be formed on a photolithography exposure mask used to manufacture semiconductor devices; and
a computer readable medium comprising optical proximity correction calculation procedures for acting upon the data, wherein at least a portion of the optical proximity correction calculation procedures are executed using the graphics processing unit, and the optical proximity correction calculation procedures comprise:
causing processing of a set of polynomials in parallel using the graphics processing unit.
2. The system of claim 1 further comprising:
output devices for displaying the results of applying the optical proximity correction calculation procedures executed using the graphics processing unit upon the data,
wherein X and Y coordinates for two opposite corners of a two-dimensional trapezoidal shape of the data is represented in a RGBA (RED-GREEN-BLUE-ALPHA) color space format in the graphics processing unit.
3. A method comprising:
providing a target mask, wherein the target mask is nonpixelated, quantized tone, and in a spatial domain;
performing a frequency domain transformation on the target mask to obtain a first mask, wherein the first mask is nonpixelated, continuous tone, and in a frequency domain; and
using an electronic processor, computing a first cost function for a first mask to obtain a first value.
4. The method of claim 3 comprising:
altering the first mask in a frequency domain to obtain a second mask, wherein the second mask is nonpixelated, continuous tone, and in the frequency domain, wherein the altering comprises:
converting a matrix into a set of polynomials; and
processing the set of polynomials using parallel computational hardware.
5. The method of claim 3 comprising:
computing the first cost function for the second mask to obtain a second value; and
repeating the altering the first mask and the computing the first cost function for the second mask until the second value is less than the first value.