Abstract: 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.

Abstract: According to one embodiment, a chemical supply device includes: a bubbler configured to contain a chemical solution which is used in a semiconductor process and to receive an input gas for vaporizing the chemical solution into an output gas; a constant-temperature bath configured to contain the bubbler and to adjust a temperature of the chemical solution; a valve module fluidically connected with the bubbler and configured to provide channels for the chemical solution, the input gas, and the output gas; a level sensor configured to detect a remaining level of the chemical solution; a controller; and a memory configured to store a program for operating the controller, and the controller is configured to determine a target flow rate of the input gas to cause a flow rate of the output gas to have a designated flow rate, based on the remaining level of the chemical solution.