Stefan Holst

Restrict Encoding for Mixed-Mode BIST

Programmable mixed-mode BIST schemes combine pseudo-random pattern testing and deterministic test. This paper presents a synthesis technique for a mixed-mode BIST scheme which is able to exploit the regularities of a deterministic test pattern set for minimizing the hardware overhead and memory requirements. The scheme saves more than 50% hardware costs compared with the best schemes known so far while complete programmability is still preserved.

A diagnosis algorithm for extreme space compaction

During volume testing, test application time, test data volume and high performance automatic test equipment (ATE) are the major cost factors. Embedded testing including built-in self-test (BIST) and multi-site testing are quite effective cost reduction techniques which may make diagnosis more complex. This paper presents a test response compaction scheme and a corresponding diagnosis algorithm which are especially suited for BIST and multi-site testing. The experimental results on industrial designs show, that test time and response data volume reduces significantly and the diagnostic resolution even improves with this scheme. A comparison with X-Compact indicates, that simple parity information provides higher diagnostic resolution per response data bit than more complex signatures.

Adaptive Debug and Diagnosis Without Fault Dictionaries

Diagnosis is essential in modern chip production to increase yield, and debug constitutes a major part in the pre-silicon development process. For recent process technologies, defect mechanisms are increasingly complex, and continuous efforts are made to model these defects by using sophisticated fault models. Traditional static approaches for debug and diagnosis with a simplified fault model are more and more limited. In this paper, a method is presented, which identifies possible faulty regions in a combinational circuit, based on its input/output behavior and independent of a fault model. The new adaptive, statistical approach is named POINTER for ‘Partially Overlapping Impact couNTER’ and combines a flexible and powerful effect-cause pattern analysis algorithm with high-resolution ATPG. We show the effectiveness of the approach through experiments with benchmark and industrial circuits. In addition, even without additional patterns this analysis method provides good resolution for volume diagnosis, too.

Generalized Fault Modeling for Logic Diagnosis

To cope with the numerous defect mechanisms in nanoelectronic technology, more and more complex fault models have been introduced. Each model comes with its own properties and algorithms for test generation and logic diagnosis. In diagnosis, however, the defect mechanisms of a failing device are not known in advance, and algorithms that assume a specific fault model may fail. Therefore, diagnosis techniques have been proposed that relax fault assumptions or even work without any fault model. In this chapter, we establish a generalized fault modeling technique and notation. Based on this notation, we describe and classify existing models and investigate the properties of a fault model independent diagnosis technique.

Structural Test for Graceful Degradation of NoC Switches

Networks-on-Chip (NoCs) are implicitly fault tolerant due to their inherent redundancy. They can overcome defective cores, links and switches. As a side effect, yield is increased at the cost of reduced performability. In this paper, a new diagnosis method based on the standard flow of industrial volume testing is presented, which is able to identify the intact functions rather than providing only a pass/fail result for the complete switch. The new method combines for the first time the precision of structural testing with information on the functional behavior in the presence of defects to determine the unaffected switch functions and use partially defective NoC switches. According to the experimental results, this improves the performability of NoCs as more than 61\% of defects only impair one switch port. Unlike previous methods for implementing fault tolerant switches, the developed technique does not impose any additional area overhead and is compatible with any switch design.

Scan Test Power Simulation on GPGPUs

The precise estimation of dynamic power consumption, power droop and temperature development during scan test require a very large number of time-aware gate-level logic simulations. Until now, such characterizations have been feasible only for rather small designs or with reduced precision due to the high computational demands. We propose a new, throughput-optimized timing simulator on running on GPGPUs to accelerate these tasks by more than two orders of magnitude and thus providing for the first time precise and comprehensive toggle data for industrial-sized designs and over long scan test operations. Hazards and pulse-filtering are supported for the first time in a GPGPU accelerated simulator, and the system can easily be extended to even more sophisticated delay and power models.

GPU-accelerated small delay fault simulation

The simulation of delay faults is an essential task in design validation and reliability assessment of circuits. Due to the high sensitivity of current nano-scale designs against smallest delay deviations, small delay faults recently became the focus of test research. Because of the subtle delay impact, traditional fault simulation approaches based on abstract timing models are not sufficient for representing small delay faults. Hence, timing accurate simulation approaches have to be utilized, which quickly become inapplicable for larger designs due to high computational requirements. In this work we present a waveform-accurate approach for fast high-throughput small delay fault simulation on Graphics Processing Units (GPUs). By exploiting parallelism from gates, faults and patterns, the proposed approach enables accurate exhaustive small delay fault simulation even for multimillion gate designs without fault dropping for the first time.

High-Throughput Logic Timing Simulation on GPGPUs

Many EDA tasks such as test set characterization or the precise estimation of power consumption, power droop and temperature development, require a very large number of time-aware gate-level logic simulations. Until now, such characterizations have been feasible only for rather small designs or with reduced precision due to the high computational demands. The new simulation system presented here is able to accelerate such tasks by more than two orders of magnitude and provides for the first time fast and comprehensive timing simulations for industrial-sized designs. Hazards, pulse-filtering, and pin-to-pin delay are supported for the first time in a GPGPU accelerated simulator, and the system can easily be extended to even more realistic delay models and further applications. A sophisticated mapping with efficient memory utilization and access patterns as well as minimal synchronizations and control flow divergence is able to use the full potential of GPGPU architectures. To provide such a mapping, we combine for the first time the versatility of event-based timing simulation and multi-dimensional parallelism used in GPU-based gate-level simulators. The result is a throughput-optimized timing simulation algorithm, which runs many simulation instances in parallel and at the same time fully exploits gate-parallelism within the circuit.

Acceleration of Monte-Carlo molecular simulations on hybrid computing architectures

Markov-Chain Monte-Carlo (MCMC) methods are an important class of simulation techniques, which execute a sequence of simulation steps, where each new step depends on the previous ones. Due to this fundamental dependency, MCMC methods are inherently hard to parallelize on any architecture. The upcoming generations of hybrid CPU/GPGPU architectures with their multi-core CPUs and tightly coupled many-core GPGPUs provide new acceleration opportunities especially for MCMC methods, if the new degrees of freedom are exploited correctly. In this paper, the outcomes of an interdisciplinary collaboration are presented, which focused on the parallel mapping of a MCMC molecular simulation from thermodynamics to hybrid CPU/GPGPU computing systems. While the mapping is designed for upcoming hybrid architectures, the implementation of this approach on an NVIDIA Tesla system already leads to a substantial speedup of more than 87× despite the additional communication overheads.

GPU-Accelerated Simulation of Small Delay Faults

Delay fault simulation is an essential task during test pattern generation and reliability assessment of electronic circuits. With the high sensitivity of current nano-scale designs toward even smallest delay deviations, the simulation of small gate delay faults has become extremely important. Since these faults have a subtle impact on the timing behavior, traditional fault simulation approaches based on abstract timing models are not sufficient. Furthermore, the detection of these faults is compromised by the ubiquitous variations in the manufacturing processes, which causes the actual fault coverage to vary from circuit instance to circuit instance, and makes the use of timing accurate methods mandatory. However, the application of timing accurate techniques quickly becomes infeasible for larger designs due to excessive computational requirements. In this paper, we present a method for fast and waveform-accurate simulation of small delay faults on graphics processing units with exceptional computational performance. By exploiting multiple dimensions of parallelism from gates, faults, waveforms, and circuit instances, the proposed approach allows for timing-accurate and exhaustive small delay fault simulation under process variation for designs with millions of gates.