Abstract: A mask topology optimization method for surface plasmon near-field photolithography, including: acquiring first mask data and performing fuzzy processing and projection processing on same to obtain second mask data; performing forward calculation according to the second mask data and a preset surface plasmon near-field photolithography condition to obtain imaging data and forward field data; calculating an imaging error between the imaging data and expected imaging data; performing adjoint calculation on the second mask data to obtain adjoint field data; calculating a gradient matrix of the imaging error relative to the first mask data according to the forward field data and the adjoint field data; and updating the first mask data according to the gradient matrix, repeating the steps for iteration calculation until the optimized mask data is obtained, and outputting a final mask pattern.

Abstract: The present disclosure provides a mask topology optimization method for surface plasmon near-field photolithography, comprising: acquiring first mask data and performing fuzzy processing and projection processing on same to obtain second mask data; performing forward calculation according to the second mask data and a preset surface plasmon near-field photolithography condition to obtain imaging data and forward field data; calculating an imaging error between the imaging data and expected imaging data; performing adjoint calculation on the second mask data to obtain adjoint field data; calculating a gradient matrix of the imaging error relative to the first mask data according to the forward field data and the adjoint field data; and updating the first mask data according to the gradient matrix, repeating the steps for iteration calculation until the optimized mask data is obtained, and outputting a final mask pattern.

Abstract: Schema-less databases can make data modeling and data management difficult and can detrimentally affect integration with an RDBMS. Inferring a schema from a schema-less database can improve integration by indicating a structure or organization of data in the schema-less database. A schema analyzer can infer a schema by processing data of the schema-less database to identify statistically significant data fields. The schema analyzer then creates a schema that comprises the statistically significant data fields. A data modeler can use the resulting schema along with a schema for a RDBMS to generate a unified data model. A user may submit a query based on the unified data model to obtain results from both databases. The data modeler translates the query from the unified model to be compatible with each of the schemas so that data may be written to or retrieved from each of the schema-less database and the RDBMS.

Abstract: Schema-less databases can make data modeling and data management difficult and can detrimentally affect integration with an RDBMS. Inferring a schema from a schema-less database can improve integration by indicating a structure or organization of data in the schema-less database. A schema analyzer can infer a schema by processing data of the schema-less database to identify statistically significant data fields. The schema analyzer then creates a schema that comprises the statistically significant data fields. A data modeler can use the resulting schema along with a schema for a RDBMS to generate a unified data model. A user may submit a query based on the unified data model to obtain results from both databases. The data modeler translates the query from the unified model to be compatible with each of the schemas so that data may be written to or retrieved from each of the schema-less database and the RDBMS.