Methods and systems for compressing shape data for electronic designs

Abstract

Methods and systems for compressing shape data for a set of electronic designs include inputting a set of shape data, where the shape data comprises mask designs. A convolutional autoencoder encodes the set of shape data, where the encoding compresses the set of shape data to produce a set of encoded shape data. The convolutional autoencoder is tuned for increased accuracy of the set of encoded shape data based on design rules for the set of electronic designs. The convolutional autoencoder comprises a set of parameters comprising weights, and the convolutional autoencoder has been trained to retain important information needed, based on the design rules for the set of electronic designs.

What is claimed is: 
     
         1 . A system for compression of shape data for a set of electronic designs, the system comprising:
 a computer processor configured to receive a set of shape data, wherein the set of shape data comprises mask designs; and   a computer processor configured to encode, using a convolutional autoencoder, the set of shape data, wherein the encoding compresses the set of shape data to produce a set of encoded shape data;   wherein the convolutional autoencoder comprises a set of adjusted parameters comprising weights, wherein the set of adjusted parameters has been tuned for increased accuracy of the set of encoded shape data using the weights, wherein the weights are used to determine what information to keep based on design rules for the set of electronic designs.   
     
     
         2 . The system of  claim 1 , wherein the encoding using the convolutional autoencoder comprises a flattening step followed by an embedding step, the embedding step involving a fully-connected embedding layer which outputs a one-dimensional vector. 
     
     
         3 . The system of  claim 2 , wherein the one-dimensional vector comprises 256 elements. 
     
     
         4 . The system of  claim 1 , wherein the convolutional autoencoder comprises a pre-determined set of convolution layers, including a kernel size and a filter size for each convolution layer in the pre-determined set of convolution layers. 
     
     
         5 . The system of  claim 4 , wherein the pre-determined set of convolution layers comprises:
 a first convolution layer using a first 5×5 kernel;   a second convolution layer following the first convolution layer and using a second 5×5 kernel;   a third convolution layer following the second convolution layer and using a first 3×3 kernel; and   a fourth convolution layer following the third convolution layer and using a second 3×3 kernel.   
     
     
         6 . The system of  claim 5 , wherein the first, second, third and fourth convolutional layers use filter sizes of 32, 64, 128 and 256, respectively. 
     
     
         7 . The system of  claim 5 , wherein a stride of 2 is used in each of the four convolution layers. 
     
     
         8 . The system of  claim 1 , further comprising a computer processor configured to decode the set of encoded shape data into decoded shape data using the convolutional autoencoder, wherein the decoded shape data reproduces the received set of shape data within a pre-determined threshold. 
     
     
         9 . The system of  claim 1 , wherein:
 the set of shape data comprises a grid of tiles decomposed from a larger image; and   the encoding comprises encoding the grid of tiles on a tile-by-tile basis, forming an encoded grid of tiles.   
     
     
         10 . The system of  claim 9 , wherein each tile in the grid of tiles comprises a halo to reduce artifacts at a boundary of the tile, the halo being a region of neighboring pixels surrounding the tile, the halo having a size chosen based on at least one of: a number of convolution layers of the convolutional autoencoder and a kernel size of the convolution layers of the convolutional autoencoder. 
     
     
         11 . The system of  claim 9 , further comprising:
 a computer processor configured to determine an error value for a tile in the encoded grid of tiles; and   a computer processor configured to output a tile in the grid of tiles instead of the tile in the encoded grid of tiles when the error value of the tile in the encoded grid of tiles is greater than a pre-determined threshold.   
     
     
         12 . The system of  claim 11 , wherein the error value is based on a distance criterion to manufacture the set of shape data on a surface, wherein the distance criterion is based on the design rules. 
     
     
         13 . The system of  claim 11 , wherein the error value is based on a difference in dose energy to manufacture the set of shape data on a surface, wherein the difference in dose energy is based on the design rules. 
     
     
         14 . The system of  claim 1 , wherein the set of shape data further comprises simulated mask designs. 
     
     
         15 . The system of  claim 1 , wherein the design rules comprise a minimum line width or a minimum line-to-line spacing. 
     
     
         16 . The system of  claim 1 , wherein the set of adjusted parameters is tuned for increased accuracy in a tradeoff of compression ratio and accuracy gain. 
     
     
         17 . A system for training a convolutional autoencoder for compression of shape data for a set of electronic designs, the system comprising:
 a computer processor configured to receive a set of shape data, wherein the set of shape data comprises mask designs;   a computer processor configured to receive a set of parameters including a set of convolution layers for the convolutional autoencoder, wherein the set of parameters is determined using design rules for the set of electronic designs, and wherein the set of parameters comprises weights;   a computer processor configured to encode the set of shape data to compress the set of shape data, using the set of convolution layers of the convolutional autoencoder, to produce a set of encoded shape data; and   a computer processor configured to adjust the set of parameters, wherein the adjusted set of parameters comprises the set of parameters tuned for increased accuracy of the set of encoded shape data, and wherein the adjusted set of parameters comprises adjusted weights to retain important information needed, based on the design rules for the set of electronic designs, to reproduce the received set of shape data.   
     
     
         18 . The system of  claim 17 , wherein the set of parameters comprises at least one of: a kernel size, a stride value and a filter size for each convolution layer. 
     
     
         19 . The system of  claim 17 , further comprising a computer processor configured to decode the set of encoded shape data into decoded data, using the convolutional autoencoder. 
     
     
         20 . The system of  claim 19 , further comprising a computer processor configured to calculate a loss by comparing the decoded data with the received set of shape data. 
     
     
         21 . The system of  claim 19  wherein:
 the set of shape data comprises a grid of tiles decomposed from a larger image; and 
 the encoding and the decoding comprise encoding and decoding the grid of tiles on a tile-by-tile basis. 
 
     
     
         22 . The system of  claim 17 , wherein the design rules comprise a minimum line width or a minimum line-to-line spacing. 
     
     
         23 . The system of  claim 17 , wherein the set of shape data further comprises simulated scanning electron microscope (SEM) images.