The slope of transient waveform is dominated by the characteristics of the discharging (or charging) path, including the path topology, the sizes and the states of MOS transistors. The slope value of transient waveform can be obtained by calculating the equivalent RC time constant of the evaluated cluster circuit, and it can be obtained efficiently by traversing the tree recursively. However, bottleneck effect always exists in the charging/discharging path and plays an important role on the charging/discharging behavior of the output. If neglect the effect, the waveform approximation technique used in BTS will give rise to a larger error in some cases. Therefore, we propose an algorithm to solve this problem.

In recently year, the analysis of power management becomes more important. It is difficult to obtain the maximum power because this is NP-complete. For an n-input circuit, there are 22n different input patterns to be considered. There are two major methods for this problem. First method is to generate input patterns to obtain the maximal power by simulating these generated patterns. This method is called pattern based. The other one uses probability method to estimate the power density of each node of a circuit to calculate the maximal power. In this paper, we use a pattern based method to estimate the maximal power. This method is better than that of probability for the simulation of power activity. In practical applications, these generated patterns can be applied and observe the activity of a circuit. These simulated data can be used to examined the critical paths for performance optimization. A simulated annealing algorithm is proposed to search input patterns for maximum power. Firstly, it transforms this problem into an optimization problem to adapt the simulated annealing method. In this method, there are three strategies for generating the next input patterns, called neighborhood. In the first strategy, it generates the next input pattern by changing the status of all input nodes. In the second strategy, some input nodes are selected and changed randomly.