Abstract: The invention discloses a pure end-to-end deep reinforcement learning for training car racing game AI bot that uses only the velocity information extracted from screen for both training and testing phases without using any internal state from game environment, such as the car facing angle. The learned AI bot can play better than the average performance of human players. In addition, the reward function is designed to consist only the velocity value, and use Ape-X distributed training framework combined with a variant of Deep Q Network to solve the sparse training signal problem caused by the reward function of an original design. Moreover, limit learner rate method is designed that improves the training efficiency and training performance. The AI bot trained in this way can achieve performance beyond the average human level and reach a level close to professional players.

Abstract: A method for fabricating a SONOS memory is disclosed. First, a semiconductor substrate is provided and a SONOS memory cell is formed on said semiconductor substrate. A passivation layer is deposited on the SONOS memory cell and a contact pad is formed on the passivation layer. Subsequently, an ultraviolet treatment is performed and an annealing process is conducted thereafter.

Abstract: A method for fabricating a SONOS memory is disclosed. First, a semiconductor substrate is provided and a SONOS memory cell is formed on said semiconductor substrate. A passivation layer is deposited on the SONOS memory cell and a contact pad is formed on the passivation layer. Subsequently, an ultraviolet treatment is performed and an annealing process is conducted thereafter.