1,893 research outputs found
Cross-layer Soft Error Analysis and Mitigation at Nanoscale Technologies
This thesis addresses the challenge of soft error modeling and mitigation in nansoscale technology nodes and pushes the state-of-the-art forward by proposing novel modeling, analyze and mitigation techniques. The proposed soft error sensitivity analysis platform accurately models both error generation and propagation starting from a technology dependent device level simulations all the way to workload dependent application level analysis
Design and debugging of multi-step analog to digital converters
With the fast advancement of CMOS fabrication technology, more and more signal-processing functions are implemented in the digital domain for a lower cost, lower power consumption, higher yield, and higher re-configurability. The trend of increasing integration level for integrated circuits has forced the A/D converter interface to reside on the same silicon in complex mixed-signal ICs containing mostly digital blocks for DSP and control. However, specifications of the converters in various applications emphasize high dynamic range and low spurious spectral performance. It is nontrivial to achieve this level of linearity in a monolithic environment where post-fabrication component trimming or calibration is cumbersome to implement for certain applications or/and for cost and manufacturability reasons. Additionally, as CMOS integrated circuits are accomplishing unprecedented integration levels, potential problems associated with device scaling – the short-channel effects – are also looming large as technology strides into the deep-submicron regime. The A/D conversion process involves sampling the applied analog input signal and quantizing it to its digital representation by comparing it to reference voltages before further signal processing in subsequent digital systems. Depending on how these functions are combined, different A/D converter architectures can be implemented with different requirements on each function. Practical realizations show the trend that to a first order, converter power is directly proportional to sampling rate. However, power dissipation required becomes nonlinear as the speed capabilities of a process technology are pushed to the limit. Pipeline and two-step/multi-step converters tend to be the most efficient at achieving a given resolution and sampling rate specification. This thesis is in a sense unique work as it covers the whole spectrum of design, test, debugging and calibration of multi-step A/D converters; it incorporates development of circuit techniques and algorithms to enhance the resolution and attainable sample rate of an A/D converter and to enhance testing and debugging potential to detect errors dynamically, to isolate and confine faults, and to recover and compensate for the errors continuously. The power proficiency for high resolution of multi-step converter by combining parallelism and calibration and exploiting low-voltage circuit techniques is demonstrated with a 1.8 V, 12-bit, 80 MS/s, 100 mW analog to-digital converter fabricated in five-metal layers 0.18-µm CMOS process. Lower power supply voltages significantly reduce noise margins and increase variations in process, device and design parameters. Consequently, it is steadily more difficult to control the fabrication process precisely enough to maintain uniformity. Microscopic particles present in the manufacturing environment and slight variations in the parameters of manufacturing steps can all lead to the geometrical and electrical properties of an IC to deviate from those generated at the end of the design process. Those defects can cause various types of malfunctioning, depending on the IC topology and the nature of the defect. To relive the burden placed on IC design and manufacturing originated with ever-increasing costs associated with testing and debugging of complex mixed-signal electronic systems, several circuit techniques and algorithms are developed and incorporated in proposed ATPG, DfT and BIST methodologies. Process variation cannot be solved by improving manufacturing tolerances; variability must be reduced by new device technology or managed by design in order for scaling to continue. Similarly, within-die performance variation also imposes new challenges for test methods. With the use of dedicated sensors, which exploit knowledge of the circuit structure and the specific defect mechanisms, the method described in this thesis facilitates early and fast identification of excessive process parameter variation effects. The expectation-maximization algorithm makes the estimation problem more tractable and also yields good estimates of the parameters for small sample sizes. To allow the test guidance with the information obtained through monitoring process variations implemented adjusted support vector machine classifier simultaneously minimize the empirical classification error and maximize the geometric margin. On a positive note, the use of digital enhancing calibration techniques reduces the need for expensive technologies with special fabrication steps. Indeed, the extra cost of digital processing is normally affordable as the use of submicron mixed signal technologies allows for efficient usage of silicon area even for relatively complex algorithms. Employed adaptive filtering algorithm for error estimation offers the small number of operations per iteration and does not require correlation function calculation nor matrix inversions. The presented foreground calibration algorithm does not need any dedicated test signal and does not require a part of the conversion time. It works continuously and with every signal applied to the A/D converter. The feasibility of the method for on-line and off-line debugging and calibration has been verified by experimental measurements from the silicon prototype fabricated in standard single poly, six metal 0.09-µm CMOS process
A Hardware Security Solution against Scan-Based Attacks
Scan based Design for Test (DfT) schemes have been widely used to achieve high fault coverage for integrated circuits. The scan technique provides full access to the internal nodes of the device-under-test to control them or observe their response to input test vectors. While such comprehensive access is highly desirable for testing, it is not acceptable for secure chips as it is subject to exploitation by various attacks. In this work, new methods are presented to protect the security of critical information against scan-based attacks. In the proposed methods, access to the circuit containing secret information via the scan chain has been severely limited in order to reduce the risk of a security breach. To ensure the testability of the circuit, a built-in self-test which utilizes an LFSR as the test pattern generator (TPG) is proposed. The proposed schemes can be used as a countermeasure against side channel attacks with a low area overhead as compared to the existing solutions in literature
Delay Measurements and Self Characterisation on FPGAs
This thesis examines new timing measurement methods for self delay characterisation of Field-Programmable Gate Arrays (FPGAs) components and delay measurement of complex circuits
on FPGAs. Two novel measurement techniques based on analysis of a circuit's output failure
rate and transition probability is proposed for accurate, precise and efficient measurement of
propagation delays. The transition probability based method is especially attractive, since
it requires no modifications in the circuit-under-test and requires little hardware resources,
making it an ideal method for physical delay analysis of FPGA circuits.
The relentless advancements in process technology has led to smaller and denser transistors
in integrated circuits. While FPGA users benefit from this in terms of increased hardware
resources for more complex designs, the actual productivity with FPGA in terms of timing
performance (operating frequency, latency and throughput) has lagged behind the potential
improvements from the improved technology due to delay variability in FPGA components
and the inaccuracy of timing models used in FPGA timing analysis. The ability to measure
delay of any arbitrary circuit on FPGA offers many opportunities for on-chip characterisation
and physical timing analysis, allowing delay variability to be accurately tracked and variation-aware optimisations to be developed, reducing the productivity gap observed in today's FPGA
designs.
The measurement techniques are developed into complete self measurement and characterisation platforms in this thesis, demonstrating their practical uses in actual FPGA hardware for
cross-chip delay characterisation and accurate delay measurement of both complex combinatorial and sequential circuits, further reinforcing their positions in solving the delay variability
problem in FPGAs
Survey of Soft Error Mitigation Techniques Applied to LEON3 Soft Processors on SRAM-Based FPGAs
Soft-core processors implemented in SRAM-based FPGAs are an attractive option for applications to be employed in radiation environments due to their flexibility, relatively-low application development costs, and reconfigurability features enabling them to adapt to the evolving mission needs. Despite the advantages soft-core processors possess, they are seldom used in critical applications because they are more sensitive to radiation than their hard-core counterparts. For instance, both the logic and signal routing circuitry of a soft-core processor as well as its user memory are susceptible to radiation-induced faults. Therefore, soft-core processors must be appropriately hardened against ionizing-radiation to become a feasible design choice for harsh environments and thus to reap all their benefits. This survey henceforth discusses various techniques to protect the configuration and user memories of an LEON3 soft processor, which is one of the most widely used soft-core processors in radiation environments, as reported in the state-of-the-art literature, with the objective of facilitating the choice of right fault-mitigation solution for any given soft-core processor
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
Quantifiable Assurance: From IPs to Platforms
Hardware vulnerabilities are generally considered more difficult to fix than
software ones because they are persistent after fabrication. Thus, it is
crucial to assess the security and fix the vulnerabilities at earlier design
phases, such as Register Transfer Level (RTL) and gate level. The focus of the
existing security assessment techniques is mainly twofold. First, they check
the security of Intellectual Property (IP) blocks separately. Second, they aim
to assess the security against individual threats considering the threats are
orthogonal. We argue that IP-level security assessment is not sufficient.
Eventually, the IPs are placed in a platform, such as a system-on-chip (SoC),
where each IP is surrounded by other IPs connected through glue logic and
shared/private buses. Hence, we must develop a methodology to assess the
platform-level security by considering both the IP-level security and the
impact of the additional parameters introduced during platform integration.
Another important factor to consider is that the threats are not always
orthogonal. Improving security against one threat may affect the security
against other threats. Hence, to build a secure platform, we must first answer
the following questions: What additional parameters are introduced during the
platform integration? How do we define and characterize the impact of these
parameters on security? How do the mitigation techniques of one threat impact
others? This paper aims to answer these important questions and proposes
techniques for quantifiable assurance by quantitatively estimating and
measuring the security of a platform at the pre-silicon stages. We also touch
upon the term security optimization and present the challenges for future
research directions
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