Abstract: The invention discloses a method for manufacture sintered anode aluminum foil. Ultrasound was used to change the stacking structure of aluminum powder on the surface of the anode aluminum foil, allowing aluminum powder particles of different sizes to form a gradient distribution in the thickness of the aluminum powder layer, with a gradual decrease in average particle size from bottom to top, larger voids in the lower layer, and denser and larger surface area in the upper layer. Therefore, the specific capacitance and the withstand voltage were improved. By introducing ultrasonic vibration, this invention can simultaneously enhance the specific capacitance and withstand voltage of anode aluminum foil after high-temperature sintering and anodizing, while traditional sintering methods cannot obtain high specific capacitance and withstand voltage performance simultaneously.

Abstract: A method for predicting the fluctuation of circuit path delay on the basis of machine learning, comprising the following steps: S1: selecting suitable sample characteristics by means of analyzing the relationship between circuit characteristics and path delay; S2: generating a random path by means of enumerating values of randomized parameters, acquiring the maximum path delay by means of performing Monte Carlo simulation on the random path, selecting a reliable path by means of the 3? standard, and using the sample characteristics and path delay of the reliable path as a sample set (D); S3: establishing a path delay prediction model, and adjusting parameters of the model; S4: verifying the precision and stability of the path delay prediction model; S5: obtaining the path delay.

Abstract: A method for predicting the fluctuation of circuit path delay on the basis of machine learning, comprising the following steps: S1: selecting suitable sample characteristics by means of analyzing the relationship between circuit characteristics and path delay; S2: generating a random path by means of enumerating values of randomized parameters, acquiring the maximum path delay by means of performing Monte Carlo simulation on the random path, selecting a reliable path by means of the 3? standard, and using the sample characteristics and path delay of the reliable path as a sample set (D); S3: establishing a path delay prediction model, and adjusting parameters of the model; S4: verifying the precision and stability of the path delay prediction model; S5: obtaining the path delay.