Abstract: A method for cleaning semiconductor substrate without damaging patterned structure on the semiconductor substrate using ultra/mega sonic device comprises applying liquid into a space between a substrate and an ultra/mega sonic device; setting an ultra/mega sonic power supply at frequency f1 and power P1 to drive the ultra/mega sonic device; before bubble cavitation in the liquid damaging patterned structure on the substrate, setting the ultra/mega sonic power supply at zero output; after temperature inside bubble cooling down to a set temperature, setting the ultra/mega sonic power supply at frequency f1 and power P1 again; detecting power on time at power P1 and frequency f1 and power off time separately or detecting amplitude of each waveform output by the ultra/mega sonic power supply; comparing the detected power on time with a preset time ?1, or comparing the detected power off time with a preset time ?2, or comparing detected amplitude of each waveform with a preset value, if the detected power on time

Abstract: A multi-parameter data type framework can, among other things, provide a more comprehensive, systematic, and/or formal mechanisms for determining an appropriate data type for a data set. For example, the multi-parameter data type framework can be used to allow analytic tools to virtually automatically figure out an appropriate data type for a set of data values.

Abstract: A system for controlling damages in cleaning a semiconductor wafer comprising features of patterned structures, the system comprising: a wafer holder for temporary restraining a semiconductor wafer during a cleaning process; an inlet for delivering a cleaning liquid over a surface of the semiconductor wafer; a sonic generator configured to alternately operate at a first frequency and a first power level for a first predetermined period of time and at a second frequency and a second power level for a second predetermined period of time, to impart sonic energy to the cleaning liquid, the first predetermined period of time and the second predetermined period of time consecutively following one another; and a controller programmed to provide the cleaning parameters, wherein at least one of the cleaning parameters is determined such that a percentage of damaged features as a result of the imparting sonic energy is lower than a predetermined threshold.

Abstract: A database query to be run against a database is received by a processor. The query includes a query predicate. The query predicate includes a condition. The condition applies to a single database table. The condition is parsed to create an input vector. The input vector is submitted to a neural network. The neural network is trained to calculate the selectivity, a number of unique values (NUV) of results of applying predicates to the single database table, and a high mode frequency (HMF) of results of applying predicates to the single database table. The neural network determines the selectivity of the query predicate, an NUV for each column in the result of applying the query predicate to the single database table, and an HMF for each column in the result of applying the query predicate to the single database table.

Abstract: A computer implemented method of matching ontologies is disclosed. The method includes, for each pair of entities of a first ontology and a second ontology, wherein each pair of entities includes a first entity from a first plurality of entities of the first ontology and a second entity from a second plurality of entities of the second ontology, and wherein the first entity and the second entity of each pair of entities is of a same data type: (1) determining a vector of similarities for the pair of entities; (2) determining a confidence score for the vector of similarities; (3) determining a relation score for each relation type based on the vector of similarities to measure relatedness between the first entity of the pair of entities and the second entity of the pair of entities; and (4) generating a mapping ontology based on the relation type, the relation score, and the confidence score of each pair of entities.

Abstract: A computer implemented method of matching ontologies is disclosed. The method includes, for each pair of entities of a first ontology and a second ontology, wherein each pair of entities includes a first entity from a first plurality of entities of the first ontology and a second entity from a second plurality of entities of the second ontology, and wherein the first entity and the second entity of each pair of entities is of a same data type: (1) determining a vector of similarities for the pair of entities; (2) determining a confidence score for the vector of similarities; (3) determining a relation score for each relation type based on the vector of similarities to measure relatedness between the first entity of the pair of entities and the second entity of the pair of entities; and (4) generating a mapping ontology based on the relation type, the relation score, and the confidence score of each pair of entities.

Abstract: A computer implemented method of matching ontologies is disclosed. The method includes, for each pair of entities of a first ontology and a second ontology, wherein each pair of entities includes a first entity from a first plurality of entities of the first ontology and a second entity from a second plurality of entities of the second ontology, and wherein the first entity and the second entity of each pair of entities is of a same data type: (1) determining a vector of similarities for the pair of entities; (2) determining a confidence score for the vector of similarities; (3) determining a relation score for each relation type based on the vector of similarities to measure relatedness between the first entity of the pair of entities and the second entity of the pair of entities; and (4) generating a mapping ontology based on the relation type, the relation score, and the confidence score of each pair of entities.