3,361 research outputs found
A Low-Cost FPGA-Based Test and Diagnosis Architecture for SRAMs
The continues improvement of manufacturing technologies allows the realization of integrated circuits containing an ever increasing number of transistors. A major part of these devices is devoted to realize SRAM blocks. Test and diagnosis of SRAM circuits are therefore an important challenge for improving quality of next generation integrated circuits. This paper proposes a flexible platform for testing and diagnosis of SRAM circuits. The architecture is based on the use of a low cost FPGA based board allowing high diagnosability while keeping costs at a very low leve
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
March Test Generation Revealed
Memory testing commonly faces two issues: the characterization of detailed and realistic fault models and the definition of time-efficient test algorithms. Among the different types of algorithms proposed for testing static random access memories, march tests have proven to be faster, simpler, and regularly structured. The majority of the published march tests have been manually generated. Unfortunately, the continuous evolution of the memory technology introduces new classes of faults such as dynamic and linked faults and makes the task of handwriting test algorithms harder and not always leading to optimal results. Although some researchers published handmade march tests able to deal with new fault models, the problem of a comprehensive methodology to automatically generate march tests addressing both classic and new fault models is still an open issue. This paper proposes a new polynomial algorithm to automatically generate march tests. The formal model adopted to represent memory faults allows the definition of a general methodology to deal with static, dynamic, and linked faults. Experimental results show that the new automatically generated march tests reduce the test complexity and, therefore, the test time, compared to the well-known state of the art in memory testin
Genetic Defect Based March Test Generation for SRAM
The continuos shrinking of semiconductor's nodes makes semiconductor memories increasingly prone to electrical defects tightly related to the internal structure of the memory. Exploring the effect of fabrication defects in future technologies, and identifying new classes of functional fault models with their corresponding test sequences, is a time consuming task up to now mainly performed by hand. This paper pro- poses a new approach to automate this procedure exploiting a dedicated genetic algorithm
Exploring the Mysteries of System-Level Test
System-level test, or SLT, is an increasingly important process step in
today's integrated circuit testing flows. Broadly speaking, SLT aims at
executing functional workloads in operational modes. In this paper, we
consolidate available knowledge about what SLT is precisely and why it is used
despite its considerable costs and complexities. We discuss the types or
failures covered by SLT, and outline approaches to quality assessment, test
generation and root-cause diagnosis in the context of SLT. Observing that the
theoretical understanding for all these questions has not yet reached the level
of maturity of the more conventional structural and functional test methods, we
outline new and promising directions for methodical developments leveraging on
recent findings from software engineering.Comment: 7 pages, 2 figure
Innovative Techniques for Testing and Diagnosing SoCs
We rely upon the continued functioning of many electronic devices for our everyday welfare,
usually embedding integrated circuits that are becoming even cheaper and smaller
with improved features. Nowadays, microelectronics can integrate a working computer
with CPU, memories, and even GPUs on a single die, namely System-On-Chip (SoC).
SoCs are also employed on automotive safety-critical applications, but need to be tested
thoroughly to comply with reliability standards, in particular the ISO26262 functional
safety for road vehicles.
The goal of this PhD. thesis is to improve SoC reliability by proposing innovative
techniques for testing and diagnosing its internal modules: CPUs, memories, peripherals,
and GPUs. The proposed approaches in the sequence appearing in this thesis are described
as follows:
1. Embedded Memory Diagnosis: Memories are dense and complex circuits which
are susceptible to design and manufacturing errors. Hence, it is important to understand
the fault occurrence in the memory array. In practice, the logical and physical
array representation differs due to an optimized design which adds enhancements to
the device, namely scrambling. This part proposes an accurate memory diagnosis
by showing the efforts of a software tool able to analyze test results, unscramble
the memory array, map failing syndromes to cell locations, elaborate cumulative
analysis, and elaborate a final fault model hypothesis. Several SRAM memory failing
syndromes were analyzed as case studies gathered on an industrial automotive
32-bit SoC developed by STMicroelectronics. The tool displayed defects virtually,
and results were confirmed by real photos taken from a microscope.
2. Functional Test Pattern Generation: The key for a successful test is the pattern applied
to the device. They can be structural or functional; the former usually benefits
from embedded test modules targeting manufacturing errors and is only effective
before shipping the component to the client. The latter, on the other hand, can be
applied during mission minimally impacting on performance but is penalized due
to high generation time. However, functional test patterns may benefit for having
different goals in functional mission mode. Part III of this PhD thesis proposes
three different functional test pattern generation methods for CPU cores embedded
in SoCs, targeting different test purposes, described as follows:
a. Functional Stress Patterns: Are suitable for optimizing functional stress during
I
Operational-life Tests and Burn-in Screening for an optimal device reliability
characterization
b. Functional Power Hungry Patterns: Are suitable for determining functional
peak power for strictly limiting the power of structural patterns during manufacturing
tests, thus reducing premature device over-kill while delivering high test
coverage
c. Software-Based Self-Test Patterns: Combines the potentiality of structural patterns
with functional ones, allowing its execution periodically during mission.
In addition, an external hardware communicating with a devised SBST was proposed.
It helps increasing in 3% the fault coverage by testing critical Hardly
Functionally Testable Faults not covered by conventional SBST patterns.
An automatic functional test pattern generation exploiting an evolutionary algorithm
maximizing metrics related to stress, power, and fault coverage was employed
in the above-mentioned approaches to quickly generate the desired patterns. The
approaches were evaluated on two industrial cases developed by STMicroelectronics;
8051-based and a 32-bit Power Architecture SoCs. Results show that generation
time was reduced upto 75% in comparison to older methodologies while
increasing significantly the desired metrics.
3. Fault Injection in GPGPU: Fault injection mechanisms in semiconductor devices
are suitable for generating structural patterns, testing and activating mitigation techniques,
and validating robust hardware and software applications. GPGPUs are
known for fast parallel computation used in high performance computing and advanced
driver assistance where reliability is the key point. Moreover, GPGPU manufacturers
do not provide design description code due to content secrecy. Therefore,
commercial fault injectors using the GPGPU model is unfeasible, making radiation
tests the only resource available, but are costly. In the last part of this thesis, we
propose a software implemented fault injector able to inject bit-flip in memory elements
of a real GPGPU. It exploits a software debugger tool and combines the
C-CUDA grammar to wisely determine fault spots and apply bit-flip operations in
program variables. The goal is to validate robust parallel algorithms by studying
fault propagation or activating redundancy mechanisms they possibly embed. The
effectiveness of the tool was evaluated on two robust applications: redundant parallel
matrix multiplication and floating point Fast Fourier Transform
Recent Trends and Perspectives on Defect-Oriented Testing
Electronics employed in modern safety-critical systems require severe qualification during the manufacturing process and in the field, to prevent fault effects from manifesting themselves as critical failures during mission operations. Traditional fault models are not sufficient anymore to guarantee the required quality levels for chips utilized in mission-critical applications. The research community and industry have been investigating new test approaches such as device-aware test, cell-aware test, path-delay test, and even test methodologies based on the analysis of manufacturing data to move the scope from OPPM to OPPB. This special session presents four contributions, from academic researchers and industry professionals, to enable better chip quality. We present results on various activities towards this objective, including device-aware test, software-based self-test, and memory test
A Review paper on the Memory Built-In Self-Repair with Redundancy Logic
The Present review paper expresses the word oriented memory test methodology for Built-In Self-Repair (BISR). To replace the defect words few logics are introduced. These logics are memory BIST logic and Wrapper logic. Whenever a test is carries on, the defected words are pointed out by its address only and these addresses are called failing address. The failing addresses are stored in the fuse box. Using fuse box it avoids the classic redundancy concept, where the RAMS has spare rows and columns. After the detection of faulty address, they are stored in redundancy logic. During test and redundancy configuration, the fuse box is connected to a scan registernbsp by this processnbsp inputnbsp and output data can be evaluated
Memory built-in self-repair and correction for improving yield: a review
Nanometer memories are highly prone to defects due to dense structure, necessitating memory built-in self-repair as a must-have feature to improve yield. Today’s system-on-chips contain memories occupying an area as high as 90% of the chip area. Shrinking technology uses stricter design rules for memories, making them more prone to manufacturing defects. Further, using 3D-stacked memories makes the system vulnerable to newer defects such as those coming from through-silicon-vias (TSV) and micro bumps. The increased memory size is also resulting in an increase in soft errors during system operation. Multiple memory repair techniques based on redundancy and correction codes have been presented to recover from such defects and prevent system failures. This paper reviews recently published memory repair methodologies, including various built-in self-repair (BISR) architectures, repair analysis algorithms, in-system repair, and soft repair handling using error correcting codes (ECC). It provides a classification of these techniques based on method and usage. Finally, it reviews evaluation methods used to determine the effectiveness of the repair algorithms. The paper aims to present a survey of these methodologies and prepare a platform for developing repair methods for upcoming-generation memories
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