Path planning of wafer probe by reinforcement learning

• Main Authors: Yu-Syuan Weng, 翁雨玄
• Other Authors: Kao-Shing Hwang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/8g6s64

碩士 === 國立中山大學 === 電機工程學系研究所 === 107 === At present, industries generally conduct wafer probing with the use of automated test equipment, and switches to manual processing when testing on odd dice. This way of testing reduces the efficiency of the test equipment. This thesis uses the method of reinforcement learning that considers the wafer as the environment and moves the testing probe by an agent. By training the agent, it is capable of testing all the dice on the wafer with the least number of actions. When the number of dice increases, however, the agent will face the problem of the state space becoming massive. In order to mitigate this problem, this thesis proposes a method called “spotlight”. This method changes the state of the environment from the testing situation of all dice to only the ones surrounding the probe in order to reduce the impact caused by the increase of the number of dice. This thesis uses the convolutional neural network to deal with the state of the environment, and the Asynchronous advantage actor-critic algorithm to train the agent to move the probe and spotlight. The agent is first trained in a smaller environment. The neural network will load the network parameters previously trained when training in a bigger environment later on. This will provide it with the experience learned in a smaller environment. In the empty environment testing, it shows that the method of spotlight can be applied to several kinds of probes, and multiple sizes of the environment. In the odd environment testing, it also shows that the method of spotlight can be applied to the odd environment.