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Transient pulses generated by high-energy particles can cause soft errors in circuits, resulting in spacecraft malfunctions and posing serious threats to the normal operation of spacecraft. For integrated circuits used in space applications, it is necessary to first evaluate soft errors caused by transient pulses. Conventional soft-error-rate evaluation tools are designed to simulate the generation of transient pulses using many accurate models, while the propagation of transient pulses is primarily simulated by circuit-level simulation tools. Due to the limitations of simulation tools, conventional evaluation approaches are limited to the circuit scale. The simulation runtime is unbearable for large-scale integrated circuits. This paper presents an approach for evaluating the soft error rate using machine learning. A back propagation neural network is implemented in the proposed approach. It helps to determine the probability of transient pulse propagation. Compared with the conventional soft-error-rate evaluation results, the proposed approach demonstrates a strong correlation in both trend and magnitude. The average difference between the results obtained using the proposed evaluation method and the experimental results is 23.5%, which is 7.5% higher than that between the results obtained using the conventional evaluation method and the experimental results. Compared to the conventional evaluation method, the proposed approach improves the runtime by an order of magnitude. The proposed approach also benefits the locating of highly sensitive circuit nodes in large-scale integrated circuits. Circuit design and radiation hardening are both useful applications. [ABSTRACT FROM AUTHOR]

To solve the problems of poor exploration ability and convergence speed of traditional deep reinforcement learning in the navigation task of the patrol robot under indoor specified routes, an improved deep reinforcement learning algorithm based on Pan/Tilt/Zoom(PTZ) image information was proposed in this paper. The obtained symmetric image information and target position information are taken as the input of the network, the speed of the robot is taken as the output of the next action, and the circular route with boundary is taken as the test. The improved reward and punishment function is designed to improve the convergence speed of the algorithm and optimize the path so that the robot can plan a safer path while avoiding obstacles first. Compared with Deep Q Network(DQN) algorithm, the convergence speed after improvement is shortened by about 40%, and the loss function is more stable. [ABSTRACT FROM AUTHOR]

IR-drop is a fundamental constraint by almost all integrated circuits (ICs) physical designs, and many iterations of timing engineer change order (ECO), IR-drop ECO, or other ECO are needed before design signoff. However, IR-drop analysis usually takes a long time and wastes so many resources. In this work, we develop a fast dynamic IR-drop predictor based on a machine learning technique, XGBoost, and the prediction method can be applied to vector-based and vectorless IR-drop analysis simultaneously. Correlation coefficient is often used to characterize the symmetry of prediction data and golden data, and our experiments show that the prediction correlation coefficient is more than 0.96 and the average error is no more than 1.3 mV for two industry designs, which are of 2.4 million and 3.7 million instances, respectively, and that the analysis is speeded up over 4.3 times compared with the IR-drop analysis by commercial tool, Redhawk. [ABSTRACT FROM AUTHOR]