This paper presents a novel abstraction-guided simulation approach for functional verification. The results of Markov analysis of the abstract model of the design under verification are used as the guidance of simulation on the concrete design. The results of the Markov analysis can offer the information about how hard it is to reach each abstract state from the initial state, and how hard it is to reach certain target states from each abstract state. Such information is able to guide the simulation in two aspects: 1) in exploring abstract state space and 2) in exercising target state. Assuming a good abstract model, experimental results show that the simulation using Markov analysis as guidance is highly efficient in both aspects.

Test generation for hard-to-reach states is important in functional verification. In this paper, we present a path constraint solving-based test generation method (PACOST) which operates in an abstraction-guided semiformal verification framework to cover hard-to-reach states. PACOST combines concrete simulation and symbolic simulation on the design under verification for path constraint extraction and mutation, and uses a sequential path constraint extractor to generate a set of valid input vectors for exploring different simulation paths with different next states. It then works on a target state-oriented abstract model to select the next state with the smallest abstract distance. In addition, the value of register variables in control logic can be controlled by analyzing the data dependence between variables, which helps the simulation converge to the target states. Experimental results show that PACOST can generate shorter traces reaching hard-to-reach states, in comparison with previous abstraction-guided semiformal methods.

This paper presents a novel method for functional test generation aiming at exploring control state space of the design. The steady-state probabilities (SP's) of the abstract design's control FSM are used to guide test generation. The SP's of the states can reflect how hard the states can be reached, and the hard-to-reach states are assigned with high priority to be exercised. Experimental results show that our method has better performance in test generation in comparison with constrained random simulation, and demonstrate that SP's provide good guidance on traversing hard-to-reach states of the design under validation.

Test generation for hard-to-reach states has been one of the hardest tasks in functional verification. In this paper, we present PACOST, a PAth Constraint Solving based Test generation method which operates in an abstraction-guided simulation framework to cover hard-to-reach states. PACOST combines concrete simulation and symbolic simulation in a path constraint solver to generate a set of valid input vectors for exploring different simulation paths, followed by next state selection considering abstract distances. In addition, two backtracking strategies are proposed to alleviate the dead end problem and ensure fast converge to the target state. Experimental results show that PACOST is effective in covering hard-to-reach states.

This paper proposes a test path selection approach for capturing delay failures caused by the accumulated distributed small delay variations. First, a universal path candidate setU, which contains testable long paths, is generated. Second, given a path number threshold, path selection fromUis performed with the objective of maximizing the capability to capture potential delay failures. The path selection problem is converted to a minimal space intersection problem, and a greedy path selection heuristics is proposed, the key point of which is to calculate the probability that all the paths in a specified path set meet the delay constraint. Statistical timing analysis technologies and heuristics are used in the calculation. Experimental results show that the proposed approach is time efficient and achieves higher probability of capturing delay failures than traditional path selection approaches.

Critical path selection plays an important role in testing of small delay defects (SDD). For some timing-balanced circuits, the numbers of candidate critical paths may be very large, and this will make Monte Carlo simulation based statistical timing analysis very inefficient. A fast path selection approach based on graph partition is proposed in this paper. First, a critical path graph (CPG) is generated to implicitly enumerate almost all candidate critical paths, and then the CPG is partitioned into several sub graphs which contain limited numbers of paths using two graph partition approaches. After that, Monte Carlo simulation is applied on each sub graph for path selection. At last, according to the partition topology of the CPG and path sets selected from each sub graph, a path set for the original CPG is generated using Union and Cartesian product operations for testing SDDs. Experimental results show that for circuits containing large numbers of candidate critical paths, the proposed path selection approach can reduce the CPU time significantly and maintain a higher probability of capturing delay failures compared to path selection methods based on general Monte Carlo simulation.

In this paper, we focus on generation of a universal path candidate set U that contains testable long paths for delay testing. Some strategies are presented to speed up the depth first search procedure of U generation, targeting the reduction of sensitization criteria checking times. Experimental results illustrate that our approach achieves an 8X speedup on average in comparison with the traditional depth first search approach.

In order to combine the power of simulation-based and formal techniques, semi-formal methods have been widely explored. Among these methods, abstraction-guided simulation is a quite promising one. In this paper, we propose an abstraction-guided simulation approach aiming to cover hard-to-reach states in functional verification of microprocessors. A Markov model is constructed utilizing the high level functional specification, i.e. ISA. Such model integrates vector correlations. Furthermore, several strategies utilizing abstraction information are proposed as an effective guidance to the test generation. Experimental results on two complex microprocessors show that our approach is more efficient in covering hard-to-reach states than similar methods. Comparing with some work with other intelligent engines, our approach could guarantee higher hit ratio of target states without efficiency loss.

We propose an efficient algorithm on generation of a universal path candidate set U that contains testable long paths for delay testing. Some strategies are presented to speed up the depth-first search procedure of U generation, targeting the reduction of checking times of sensitization criteria. Experimental results illustrate that our approach achieves an 8X speedup on average in comparison with the traditional depth-first search approach.

Statistical timing models have been proposed to describe delay variations in very deep sub-micron process technologies, which have increasingly significant influence on circuit performance. Under a statistical timing model, testing of a path can detect potential delay failures caused by different small delay defects. Due to path correlations, the potential delay failures captured by two different paths overlap between each other more or less. It is difficult to find a given number of paths that can capture most potential delay failures. In this paper, the path selection problem is converted to a minimal space intersection problem, and a greedy path selection heuristics is proposed, the key point of which is to calculate the probability that the paths in a specified path set all meet the delay constraint. Statistical timing analysis technologies and heuristics are used in the calculation. Experimental results show that the proposed approach is time-efficient and achieves a higher probability of capturing delay failures in comparison with conventional path selection approaches.

As the design complexity increases dramatically, results of functional simulation are usually checked through only a part of signals during design verification. It is important, therefore, to consider the observability of internal signals for effective checking. This paper proposes a static observability analysis method to automatically select internal observation signals, which improves the quality of functional verification. A series of formulas are defined to evaluate observability of internal signals, and an algorithm is proposed to locate the sources of low-observability. Such sources, rather than general hard-to-observe signals, are desirable internal observation signals. Experimental results indicate that signals selected by this method can improve the observability of designs more than those randomly selected from hard-to-observe signals.

In this paper we propose an observability branch coverage metric (OBCM) based on dynamic factored use-define chains, along with its evaluation method. This technique exploits the efficiency of data-flow analysis rather than methods like fault simulation. Hence it can be easily integrated into HDL compilers or simulators. Experimental results show that OBCM can provide more meaningful coverage data for functional verification than traditional branch coverage metric (BCM).

Along with more pre-designed and pre-verified cores are integrated into a single chip to construct an entire system, the test application time increases significantly. This paper presents a novel test scheduling solution, unlike previous techniques that take advantage of balanced scan chains of every single core, utilizing the balance of pairwise combined cores. Experimental results for two ITC '02 SOC benchmarks show that the pair balance-based test scheduling technique achieves less test time compared to the previous approaches.

Coverage evaluation is indispensable for simulate modern designs. In this paper, we present an efficient algorithm to evaluate observability coverage, which is based on Factored Use-Def chains (FUD chains), a data-flow analysis technique in compilers. With the strategy of enhanced FUD chains and event-driven analysis, this method has three advantages. First, it is significantly more computationally efficient than prior efforts to assess observability information. Secondly, it could be easily integrated into hardware description language (HDL) compilers or simulators. Finally, it is universal, and can be combined with controllability metrics, such as statement coverage metric (SCM).