Memory read faults: taxonomy and automatic test generation

This paper presents an innovative algorithm for the automatic generation of March tests. The proposed approach is able to generate an optimal March test for an unconstrained set of memory faults in very low computation time. Moreover, we propose a new complete taxonomy for memory read faults, a class of faults never carefully addressed in the past

Automatic March tests generation for static and dynamic faults in SRAMs

New memory production modern technologies introduce new classes of faults usually referred to as dynamic memory faults. Although some hand-made March tests to deal with these new faults have been published, the problem of automatically generate March tests for dynamic faults has still to be addressed, in this paper we propose a new approach to automatically generate March tests with minimal length for both static and dynamic faults. The proposed approach resorts to a formal model to represent faulty behaviors in a memory and to simplify the generation of the corresponding tests

Automatic March tests generation for multi-port SRAMs

Testing of Multi-Port (MP) SRAMs requires special tests since the multiple and simultaneous access can sensitize faults that are different from the conventional single-port memory faults. In spite of their growing use, few works have been published on testing MP memories. In addition, most of the published work concentrated only on two ports memories (i.e., 2P memories). This paper presents a methodology to automatically generate march tests for MP memories. It is based on generations of single port memory march test firstly, then extending it to test a generic MP SRAMs. A set of experimental results shows the effectiveness of the proposed solutio

Automating defects simulation and fault modeling for SRAMs

The continues improvement in manufacturing process density for very deep sub micron technologies constantly leads to new classes of defects in memory devices. Exploring the effect of fabrication defects in future technologies, and identifying new classes of realistic functional fault models with their corresponding test sequences, is a time consuming task up to now mainly performed by hand. This paper proposes a new approach to automate this procedure. The proposed method exploits the capabilities of evolutionary algorithms to automatically identify faulty behaviors into defective memories and to define the corresponding fault models and relevant test sequences. Target defects are modeled at the electrical level in order to optimize the results to the specific technology and memory architecture

An optimal algorithm for the automatic generation of March tests

Among the different types of algorithms proposed to test random access memories (RAM), March tests have proven to be faster, simpler, regularly structured and linear in complexity. A March test consists of a sequence of March elements, each composed of a sequence of basic read/write operations to be performed on each cell of the memory, in either ascending or descending order, before proceeding to the next memory cell. The complexity of a March test is given by the number of memory operations in all March elements performed on each memory cell. This paper presents an innovative algorithm for the automatic generation of March tests. The proposed approach is able to generate an optimal March test for an unconstrained set of memory faults in very low computation time

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them