OASIS

Abstract

Small delay faults may be an indicator of a reliability threat, even if they do not affect the system functionality yet. In recent years, Faster-than-at-Speed-Test (FAST) has become a feasible method to detect faults, which are hidden by the timing slack or by long critical paths in the combinational logic. FAST poses severe challenges to the automatic test equipment with respect to timing, performance, and resolution. In this paper, it is shown how logic built-in self-test (BIST) or embedded deterministic test can be used for an efficient FAST application. Running BIST just at a higher frequency is not an option, as outputs of long paths will receive undefined values due to set time violations and destroy the content of the signature registers. Instead, for a given test pattern sequence, faults are classified according to the optimal detection frequency. For each class, a MISR-based compaction scheme is adapted, such that the critical bits to be observed can be determined by algebraic computations. Experiments show that rather a small number of inter-mediate signatures have to be evaluated to observe a large fraction of hidden delay faults testable by the given test sequence.

Abstract

As the technology shrinks, nonfunctional properties (NFPs) such as reliability, vulnerability, power consumption, or heat dissipation become as important as system functionality. As NFPs often influence each other, depend on the application and workload of a system, and exhibit nonlinear behavior, NFP simulation over long periods of system operation is computationally expensive, if feasible at all. This article presents a piecewise evaluation method for efficient NFP simulation. Simulation time is divided into intervals called evaluation windows, within which the NFP models are partially linearized. High-speed functional system simulation is achieved by parallel execution of models at different levels of abstraction. A trade-off between simulation speed and accuracy is met by adjusting the size of the evaluation window. As an example, the piecewise evaluation technique is applied to analyze aging caused by two mechanisms, namely Negative Bias Temperature Instability (NBTI) and Hot Carrier Injection (HCI), in order to identify reliability hotspots. Experiments show that the proposed technique yields considerable simulation speedup at a marginal loss of accuracy.

Abstract

Small delay faults may be an indicator of a reliability threat, even if they do not affect the system functionality yet. In recent years, Faster-than-at-Speed-Test (FAST) has become a feasible method to detect faults, which are hidden by the timing slack or by long critical paths in the combinational logic. FAST poses severe challenges to the automatic test equipment with respect to timing, performance, and resolution. In this paper, it is shown how logic built-in self-test (BIST) or embedded deterministic test can be used for an efficient FAST application. Running BIST just at a higher frequency is not an option, as outputs of long paths will receive undefined values due to set time violations and destroy the content of the signature registers. Instead, for a given test pattern sequence, faults are classified according to the optimal detection frequency. For each class, a MISR-based compaction scheme is adapted, such that the critical bits to be observed can be determined by algebraic computations. Experiments show that rather a small number of inter-mediate signatures have to be evaluated to observe a large fraction of hidden delay faults testable by the given test sequence.

Abstract

As the technology shrinks, nonfunctional properties (NFPs) such as reliability, vulnerability, power consumption, or heat dissipation become as important as system functionality. As NFPs often influence each other, depend on the application and workload of a system, and exhibit nonlinear behavior, NFP simulation over long periods of system operation is computationally expensive, if feasible at all. This article presents a piecewise evaluation method for efficient NFP simulation. Simulation time is divided into intervals called evaluation windows, within which the NFP models are partially linearized. High-speed functional system simulation is achieved by parallel execution of models at different levels of abstraction. A trade-off between simulation speed and accuracy is met by adjusting the size of the evaluation window. As an example, the piecewise evaluation technique is applied to analyze aging caused by two mechanisms, namely Negative Bias Temperature Instability (NBTI) and Hot Carrier Injection (HCI), in order to identify reliability hotspots. Experiments show that the proposed technique yields considerable simulation speedup at a marginal loss of accuracy.

Abstract

Unknown (X) values in a circuit impair test quality and increase test costs. Classical n-valued algorithms for fault simulation and ATPG, which typically use a three- or four-valued logic for the good and faulty circuit, are in principle pessimistic in presence of X-values and cannot accurately compute the achievable fault coverage. In partial scan or pipelined circuits, X-values originate in non-scan flip-flops. These circuits are tested using multi-cycle tests. Here we present multi-cycle test generation techniques for circuits with X-values due to partial scan or other X-sources. The proposed techniques have been integrated into a multi-cycle ATPG framework which employs formal Boolean and quantified Boolean (QBF) satisfiability techniques to compute the possible signal states in the circuit accurately. Efficient encoding of the problem instance ensures reasonable runtimes. We show that in presence of X-values, the detection of stuck-at faults requires not only exact formal reasoning in a single cycle, but especially the consideration of multiple cycles for excitation of the fault site as well as propagation and controlled reconvergence of fault effects. For the first time, accurate deterministic ATPG for multi-cycle test application is supported for stuck-at faults. Experiments on ISCAS'89 and industrial circuits with X-sources show that this new approach increases the fault coverage considerably.

Abstract

Unknown (X) values in a circuit impair test quality and increase test costs. Classical n-valued algorithms for fault simulation and ATPG, which typically use a three- or four-valued logic for the good and faulty circuit, are in principle pessimistic in presence of X-values and cannot accurately compute the achievable fault coverage. In partial scan or pipelined circuits, X-values originate in non-scan flip-flops. These circuits are tested using multi-cycle tests. Here we present multi-cycle test generation techniques for circuits with X-values due to partial scan or other X-sources. The proposed techniques have been integrated into a multi-cycle ATPG framework which employs formal Boolean and quantified Boolean (QBF) satisfiability techniques to compute the possible signal states in the circuit accurately. Efficient encoding of the problem instance ensures reasonable runtimes. We show that in presence of X-values, the detection of stuck-at faults requires not only exact formal reasoning in a single cycle, but especially the consideration of multiple cycles for excitation of the fault site as well as propagation and controlled reconvergence of fault effects. For the first time, accurate deterministic ATPG for multi-cycle test application is supported for stuck-at faults. Experiments on ISCAS'89 and industrial circuits with X-sources show that this new approach increases the fault coverage considerably.

Abstract

Non-functional properties (NFPs) of integrated circuits include reliability, vulnerability, power consumption or heat dissipation. Accurate NFP prediction over long periods of system operation poses a great challenge due to prohibitive simulation costs. For instance, in case of aging estimation, the existing low-level models are accurate but not efficient enough for simulation of complex designs. On the other hand, existing techniques for fast high-level simulation do not provide enough details for NFP analysis. The goal of this paper is to bridge this gap by combining the accuracy of low-level models with high-level simulation speed. We introduce an efficient mixed-level NFP prediction methodology that considers both the structure and application of a system. The system is modeled at transaction-level to enable high simulation speed. To maintain accuracy, NFP assessment for cores under analysis is conducted at gate-level by cycle-accurate simulation. We propose effective techniques for cross-level synchronization and idle simulation speed-up. As an example, we apply the technique to analyze aging caused by Negative Bias Temperature Instability in order to identify reliability hot spots. As case studies, several applications on an SoC platform are analyzed. Compared to conventional approaches, the proposed method is from 7 up to 400 times faster with mean error below 0.006%.

Abstract

Non-functional properties (NFPs) of integrated circuits include reliability, vulnerability, power consumption or heat dissipation. Accurate NFP prediction over long periods of system operation poses a great challenge due to prohibitive simulation costs. For instance, in case of aging estimation, the existing low-level models are accurate but not efficient enough for simulation of complex designs. On the other hand, existing techniques for fast high-level simulation do not provide enough details for NFP analysis. The goal of this paper is to bridge this gap by combining the accuracy of low-level models with high-level simulation speed. We introduce an efficient mixed-level NFP prediction methodology that considers both the structure and application of a system. The system is modeled at transaction-level to enable high simulation speed. To maintain accuracy, NFP assessment for cores under analysis is conducted at gate-level by cycle-accurate simulation. We propose effective techniques for cross-level synchronization and idle simulation speed-up. As an example, we apply the technique to analyze aging caused by Negative Bias Temperature Instability in order to identify reliability hot spots. As case studies, several applications on an SoC platform are analyzed. Compared to conventional approaches, the proposed method is from 7 up to 400 times faster with mean error below 0.006%.