This paper presents HITEC, a sequential circuit test generation package to generate test patterns for sequential circuits, without assuming the use of scan techniques or a reset state Several new techniques are introduced to improve the performance of test generation A targeted D element technique is presented, which greatly increases the number of possible mandatory assignments and reduces the over-specification of state variables which can sometimes result when using a standard PODEM algorithm A technique to use the state knowledge of previously generated vectors for state justification, without the memory overhead of a state transition diagram is presented For faults that were aborted during the standard test generation phase, knowledge that was gained about fault propagation, by the fault simulator, is used These techniques, when used together, produce the best published results for the ISCAS89 sequential benchmark circuits

HITEC: a test generation package for sequential circuits

Animal brains of all sizes, from the smallest to the largest, work in broadly similar ways. Studying the brain of any one animal in depth can thus reveal the general principles behind the workings of all brains. The fruit fly Drosophila is a popular choice for such research. With about 100,000 neurons – compared to some 86 billion in humans – the fly brain is small enough to study at the level of individual cells. But it nevertheless supports a range of complex behaviors, including navigation, courtship and learning. Thanks to decades of research, scientists now have a good understanding of which parts of the fruit fly brain support particular behaviors. But exactly how they do this is often unclear. This is because previous studies showing the connections between cells only covered small areas of the brain. This is like trying to understand a novel when all you can see is a few isolated paragraphs. To solve this problem, Scheffer, Xu, Januszewski, Lu, Takemura, Hayworth, Huang, Shinomiya et al. prepared the first complete map of the entire central region of the fruit fly brain. The central brain consists of approximately 25,000 neurons and around 20 million connections. To prepare the map – or connectome – the brain was cut into very thin 8nm slices and photographed with an electron microscope. A three-dimensional map of the neurons and connections in the brain was then reconstructed from these images using machine learning algorithms. Finally, Scheffer et al. used the new connectome to obtain further insights into the circuits that support specific fruit fly behaviors. The central brain connectome is freely available online for anyone to access. When used in combination with existing methods, the map will make it easier to understand how the fly brain works, and how and why it can fail to work correctly. Many of these findings will likely apply to larger brains, including our own. In the long run, studying the fly connectome may therefore lead to a better understanding of the human brain and its disorders. Performing a similar analysis on the brain of a small mammal, by scaling up the methods here, will be a likely next step along this path.

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A connectome and analysis of the adult Drosophila central brain

VLSI Test Principles and Architectures: Design for Testability (Systems on Silicon)

Designing at higher levels of abstraction is key to managing the complexity of today's VLSI chips. The authors show how they reverse-engineered the ISCAS-85 benchmarks to add a useful, new high-level tool to the designer's arsenal.

Unveiling the ISCAS-85 benchmarks: a case study in reverse engineering

New design flows require reducing work at the gate level and performing most activities before the synthesis step, including evaluation of testability of circuits. We propose a suite of RT-level benchmarks that help improve research in high-level ATPG tools. First results on the benchmarks obtained with our prototype tool show the feasibility of the approach.

RT-level ITC'99 benchmarks and first ATPG results

A set of 31 digital sequential circuits described at the gate level is presented. These circuits extend the size and complexity of the ISCAS'85 set of combinational circuits and can serve as benchmarks for researchers interested in sequential test generation, scan-based test generation, and mixed sequential/scan-based test generation using partial scan techniques. Although all the benchmark circuits are sequential, synchronous, and use only D-type flip-flops, additional interior faults and asynchronous behavior can be introduced by substituting for some or all of the flip-flops their appropriate functional models. The standard functional model of the D flip-flop provides a reference point that is independent of the faults particular to the flip-flop implementation. A testability profile of the benchmarks in the full-scan-mode configuration is discussed. >

Combinational profiles of sequential benchmark circuits

We consider instances of bipartite graphs and a number of asymptotic performance experiments in three projects: (1) top movie lists, (2) maximum matchings, and (3) minimum set covers. Experiments are designed to measure the asymptotic runtime performance of abstract data types (ADTs) in three programming languages: Java, R, and C++. The outcomes of these experiments may be surprising. In project (1), the best ADT in R consistently outperforms all ADTs in public domain Java libraries, including the library from Google. The largest movie list has 2 titles. In project (2), the FordFulkerson algorithm implementation in R significantly outperforms Java. The hardest instance has 88452 rows and 729 columns. In project (3), a stochastic version of a greedy algorithm in R can significantly outperform a state-of-the-art stochastic solver in C++ on instances with num rows ≥ 300 and num columns ≥ 3000.

F. Brglez

Papers

Combinational profiles of sequential benchmark circuits

Asymptotic Experiments with Data Structures: Bipartite Graph Matchings and Covers