The invention provides a differential pair ordered escape wiring method based on improved Monte Carlo tree search, and aims to solve the problem of path optimization of differential pair
signal wiring in the design of a high-density
printed circuit board. According to the method, MCTS and Q-learning
reinforcement learning technologies are combined, a wiring
intermediate point does not need to be preset, and a wiring path can be dynamically generated under a grid
pin array and a staggered
pin array. Through multiple times of
simulation and
backtracking, the method can effectively process complex pin arrangement and blocking areas, ensures wiring length matching and reduces
electromagnetic interference. Meanwhile, according to the method, a Q table obtained through one-time
Q learning can serve as the basis for
simulation of all the nodes of the Monte Carlo tree, all the expansion nodes are simulated in the
simulation stage, and the defect that the time of the MCTS simulation part is long is overcome. According to the method, the MCTS search capability and the convergence speed are improved by adopting a gradual widening strategy. The method is suitable for PCB design of high-performance
electronic equipment, such as the fields of communication, computers and
avionics.