Efficient Static Compaction of Test Patterns Using Partial Maximum Satisfiability

Static compaction methods aim at finding unnecessary test patterns to reduce the size of the test set as a post-process of test generation.Techniques based on partial maximum satisfiability are often used to track many hard problems in various domains,including artificial intelligence,computational biology,data mining,and machine learning.We observe that part of the test patterns generated by the commercial Automatic Test Pattern Generation(ATPG)tool is redundant,and the relationship between test patterns and faults,as a significant information,can effectively induce the test patterns reduction process.Considering a test pattern can detect one or more faults,we map the problem of static test compaction to a partial maximum satisfiability problem.Experiments on ISCAS89,ISCAS85,and ITC99 benchmarks show that this approach can reduce the initial test set size generated by TetraMAX18 while maintaining fault coverage.

Dependence of single event upsets sensitivity of low energy proton on test factors in 65 nm SRAM

In order to accurately predict the single event upsets （SEU） rate of on-orbit proton, the influence of the proton energy distribution, incident angle, supply voltage, and test pattern on the height, width, and position of SEU peak of low energy protons （LEP） in 65 nm static random access memory （SRAM） are quantitatively evaluated and analyzed based on LEP testing data and Monte Carlo simulation. The results show that different initial proton energies used to degrade the beam energy will bring about the difference in the energy distribution of average proton energy at the surface and sensitive region of the device under test （DUT）, which further leads to significant differences including the height of SEU peak and the threshold energy of SEU. Using the lowest initial proton energy is extremely important for SEU testing with low energy protons. The proton energy corresponding to the SEU peak shifts to higher average proton energies with the increase of the tilt angle, and the SEU peaks also increase significantly. The reduction of supply voltage lowers the critical charge of SEU, leading to the increase of LEP SEU cross section. For standard 6-transitor SRAM with bit-interleaving technology, SEU peak does not show clear dependence on three test patterns of logical checkerboard 55H, all＂ 1＂, and all ＂0＂. It should be noted that all the SEUs in 65 nm SRAM are single cell upset in LEP testing due to proton＇s low linear energy transfer （LET） value.

A Test Pattern Identification Algorithm and Its Application to CINRAD/SA(B) Data

A variety of faulty radar echoes may cause serious problems with radar data applications,especially radar data assimilation and quantitative precipitation estimates.In this study,＂test pattern＂ caused by test signal or radar hardware failures in CINRAD （China New Generation Weather Radar） SA and SB radar operational observations are investigated.In order to distinguish the test pattern from other types of radar echoes,such as precipitation,clear air and other non-meteorological echoes,five feature parameters including the effective reflectivity data percentage （Rz）,velocity RF （range folding） data percentage （RRF）,missing velocity data percentage （RM）,averaged along-azimuth reflectivity fluctuation （RNr,z） and averaged along-beam reflectivity fluctuation （RNa,z） are proposed.Based on the fuzzy logic method,a test pattern identification algorithm is developed,and the statistical results from all the different kinds of radar echoes indicate the performance of the algorithm.Analysis of two typical cases with heavy precipitation echoes located inside the test pattern are performed.The statistical results show that the test pattern identification algorithm performs well,since the test pattern is recognized in most cases.Besides,the algorithm can effectively remove the test pattern signal and retain strong precipitation echoes in heavy rainfall events.

Comparative Study on Physical Fitness Test Models of Chinese, Japanese and American Students

The physical health of adolescents is related to the future of the nation and the competitiveness of the country.Through a comparative analysis of the backgrounds,organizations,testing programs and evolution processes of physical health tests in China,Japan and the United States,the three countries are explored.The development trends and problems of student physical fitness tests,find the differences,learn about the research results and experiences of physical fitness tests in Japan and the United States,draw on Japanese and American management models and successful cases,and propose some methods to optimize and improve China's physical fitness test models.It is recommended to make full use of the existing resources to promote the improvement of students'physical fitness.

Enhancing SAT-Based Test Pattern Generation

This paper presents modeling tools based on Boolean satisfiability (SAT) to solve problems of test generation for combinational circuits. It exploits an added layer to maintain circuit-related information and value justification relations to a generic SAT algorithm. It dovetails binary decision graphs (BDD) and SAT techniques to improve the efficiency of automatic test pattern generation (ATPG). More specifically, it first exploits inexpensive reconvergent fanout analysis of circuit to gather information on the local signal correlation by using BDD learning, then uses the above learned information to restrict and focus the overall search space of SAT-based ATPG. Its learning technique is effective and lightweight. The experimental results demonstrate the effectiveness of the approach.

SACSR:a low power BIST method for sequential circuits

A novel built-in-self-test(BIST) method called seeded autonomous cyclic shift register (SACSR) is presented to reduce test power of the sequential circuit. The key idea is to use a pseudorandom pattern generator and several XOR gates to generate seeds that share fewer test vectors. The generated seed is taken XOR operation with a cyclic shift register, and the single input change (SIC) sequence is generated. The proposed scheme is easily implemented and can reduce the switching activities of the circuit under test (CUT) greatly. Experimental results on ISCAS89 benchmarks show that on average more than 63% power reduction can be achieved. It also demonstrates that the generated test vectors attain high fault coverage for stuck-at fault and transition fault coverage with short test length.

Investigation into the automatic recognition of time series precursor of earthquakes

In this paper, a new method of quantitative description of earthquake precursors is proposed; by this method, the precursory pattern of time series can be quantitatively described with a two-dimensional matrix. On this basis, a method of automatic recognition or automatic acquirement of precursory pattern, called simply the AA method, is put forward. Then, taking North China region as an example, various seismological precursors such as the frequency, energy, b -value, etc . and various nonlinear parameter precursors such as the capacity dimension, information dimension, correlation dimension, Hurst index and its difference, etc. are analyzed and the 8 time series so obtained are recognized automatically using the proposed precursory pattern and AA method. Besides, C-method tests and very rigorous HF (history and future) tests are made. The result shows that the R-value of prediction efficacy assessment is fairly high.

HTDet:A Clustering Method Using Information Entropy for Hardware Trojan Detection

Hardware Trojans(HTs)have drawn increasing attention in both academia and industry because of their significant potential threat.In this paper,we propose HTDet,a novel HT detection method using information entropybased clustering.To maintain high concealment,HTs are usually inserted in the regions with low controllability and low observability,which will result in that Trojan logics have extremely low transitions during the simulation.This implies that the regions with the low transitions will provide much more abundant and more important information for HT detection.The HTDet applies information theory technology and a density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise(DBSCAN)to detect all suspicious Trojan logics in the circuit under detection.The DBSCAN is an unsupervised learning algorithm,that can improve the applicability of HTDet.In addition,we develop a heuristic test pattern generation method using mutual information to increase the transitions of suspicious Trojan logics.Experiments on circuit benchmarks demonstrate the effectiveness of HTDet.

Simulation of ATPG Neural Network and Its Experimental Results

This paper first establishes a neural network model for logic circuits fromthe truth table by using linear equations theory, presents a kind of ATPG neuralnetwork model, and investigates energy local minima for the network- And then,it proposes the corresponding techniques to reduce the number of energy localminima as well as some approaches to escaping from local minimum of eliergyFinally, two simulation systems, the binary ATPG neural network and thecontinuous ATPG neural network, are implemented oli SUN 3/260 workstationin C language. The experimental results and their analysis and discussion aregiven. The preliminary experimental results show that this method is feasibleand promising.