Machine learning based automatic routing method and apparatus for semiconductor equipment
The present disclosure relates to a machine learning-based automatic routing method and apparatus for semiconductor equipment, and the machine learning-based automatic routing method for semiconductor equipment according to one embodiment of the present disclosure includes: a first operation of disposing semiconductor equipment and ancillary equipment; a second operation of recognizing connection points (points of connection, POC) which are three-dimensional (3D) coordinates of the semiconductor equipment and the ancillary equipment; and a third operation of generating an optimal path which connects the connection points (POC) using a machine learning algorithm.
1 . A machine learning-based automatic routing method for semiconductor equipment using a machine learning-based automatic routing apparatus for semiconductor equipment, the method comprising:
a first operation of disposing semiconductor equipment and ancillary equipment respectively on floors;
a second operation of recognizing connection points (points of connection, POC) which are three-dimensional (3D) coordinates of the semiconductor equipment and the ancillary equipment respectively disposed on the floors; and
a third operation of generating an optimal path which connects the connection points (POC) which are the 3D coordinates of the semiconductor equipment and the ancillary equipment respectively disposed at the sides using a machine learning algorithm.
2 . The method of claim 1 , wherein connection points of an input and an output of the semiconductor equipment and the ancillary equipment are recognized in the second operation.
3 . The method of claim 1 , wherein an optimal path which connects the connection points (POC) based on the semiconductor equipment using the machine learning algorithm is generated in the third operation.
4 . The method of claim 3 , wherein an optimal path which connects the connection points (POC) based on the semiconductor equipment using a Dijkstra algorithm is generated in the third operation.
5 . The method of claim 1 , wherein an optimal path is generated by maintaining a correlation between the connection points (POC) which are the 3D coordinates of the semiconductor equipment and the ancillary equipment respectively disposed at the sides using the machine learning algorithm when the semiconductor equipment and the ancillary equipment are moved in the third operation.
6 . A machine learning-based automatic routing apparatus for semiconductor equipment, comprising:
an equipment module disposition unit configured to dispose semiconductor equipment and ancillary equipment respectively on floors;
a connection point recognition unit configured to recognize connection points (POC) which are 3D coordinates of the semiconductor equipment and the ancillary equipment respectively disposed on the floors; and
an optimal path generation unit configured to generate an optimal path which connects the connection points (POC) which are the 3D coordinates of the semiconductor equipment and the ancillary equipment respectively disposed at the sides using a machine learning algorithm.
7 . The apparatus of claim 6 , wherein the connection point recognition unit recognizes connection points of an input and an output of the semiconductor equipment and the ancillary equipment, and
the optimal path generation unit generates an optimal path which connects the connection points (POC) based on the semiconductor equipment using a Dijkstra algorithm.