Robust configurable system design with built-in self-healing

The new generations of SRAM-based FPGA (Field Programmable Gate Array) devices, built on nanometre technology, are the preferred choice for the implementation of reconfigurable computing platforms. However, their vulnerability to hard and soft errors is a major weakness to robust system design based on FPGAs. In this paper, a novel Built-In Self-Healing (BISH) methodology, based on modular redundancy and on selfreconfiguration, is proposed. A soft microprocessor core implemented in the FPGA is responsible for the management and execution of all the BISH procedures. Fault detection and diagnosis is followed by repairing actions, taking advantage of the self-configuration features. Meanwhile, modular redundancy assures that the system still works correctly. This approach leads to a robust system design able to assure high reliability, availability and data integrity

Restoring Reliability in Fault Tolerant Reconfigurable Systems

The new generations of SRAM-based FPGAdevices, built on nanometer technology, are thepreferred choice for the implementation ofreconfigurable computing platforms. However,smaller technological scales increase theirvulnerability to manufacturing imperfections andhence to the occurrence of electromigration.Moreover, the large internal RAM (for configurationpurposes or as embedded memory blocks) makesthem more prone to soft errors.The incorporation of self-reconfigurationcapabilities in recent FPGAs, allied to the use of softand hard microprocessor cores, facilitates the offsetof these vulnerabilities by enabling the developmentof self-restoring fault tolerant reconfigurablesystems. In the methodology presented in this paper,the embedded microprocessor is also responsible forthe implementation of online self-test-and-repairstrategies, based on modular redundancy and onself-reconfiguration. The detection of faults, causedby soft or hard errors, may be followed by repairingactions, depending on the fault type. This approachleads to smoother system degradation, extending itslifetime and improving its reliability

Formal transformation methods for automated fault tree generation from UML diagrams

With a growing complexity in safety critical systems, engaging Systems Engineering with System Safety Engineering as early as possible in the system life cycle becomes ever more important to ensure system safety during system development. Assessing the safety and reliability of system architectural design at the early stage of the system life cycle can bring value to system design by identifying safety issues earlier and maintaining safety traceability throughout the design phase. However, this is not a trivial task and can require upfront investment. Automated transformation from system architecture models to system safety and reliability models offers a potential solution. However, existing methods lack of formal basis. This can potentially lead to unreliable results. Without a formal basis, Fault Tree Analysis of a system, for example, even if performed concurrently with system design may not ensure all safety critical aspects of the design. [Continues.]</div

FPGA BASED SELF-HEALING STRATEGY FOR SYNCHRONOUS SEQUENTIAL CIRCUITS

The paper develops an efficient mechanism with a view to healing bridging faults in synchronous sequential circuits. The scheme inserts faults randomly into the system at the signal levels, encompasses ways to intrigue the state of the signals and carries it with steps to rig out the true values at the primary output lines. The attempts espouse the ability of the methodology to explore the occurrence of a variety of single and multiple bridging faults and arrive at the true output. The approach enables to detect the occurrence of wired-OR and wired AND bridging faults in the combinational part of the serial binary adder as the CUT and heal both the inter and intra-gate faults through the use of the proposed methodology. It allows claiming a lower area overhead and computationally a sharp increase in the fault coverage area over the existing Triple Modular Redundancy (TMR) technique. The Field Programmable Gate Arrays (FPGA) based Spartan architecture operates through Very High-Speed Integrated Circuit Hardware Description Language (VHDL) to synthesize the Modelsim code for validating the simulation exercises. The claim incites to increase the reliability of the synchronous sequential circuits and espouse a place for the use of the strategy in the digital world

Design of On-Chip Self-Testing Signature Register

Over the last few years, scan test has turn out to be too expensive to implement for industry standard designs due to increasing test data volume and test time. The test cost of a chip is mainly governed by the resource utilization of Automatic Test Equipment (ATE). Also, it directly depends upon test time that includes time required to load test program, to apply test vectors and to analyze generated test response of the chip. An issue of test time and data volume is increasingly appealing designers to use on-chip test data compactors, either on input side or output side or both. Such techniques significantly address the former issues but have little hold over increasing number of input-outputs under test mode. Further, test pins on DUT are increasing over the generations. Thus, scan channels on test floor are falling short in number for placement of such ICs. To address issues discussed above, we introduce an on-chip self-testing signature register. It comprises a response compactor and a comparator. The compactor compacts large chunk of response data to a small test signature whereas the comparator compares this test signature with desired one. The overall test result for the design is generated on single output pin. Being no storage of test response is demanded, the considerable reduction in ATE memory can be observed. Also, with only single pin to be monitored for test result, the number of tester channels and compare edges on ATE side significantly reduce at the end of the test. This cuts down maintenance and usage cost of test floor and increases its life time. Furthermore reduction in test pins gives scope for DFT engineers to increase number of scan chains so as to further reduce test time

Fault simulation for structural testing of analogue integrated circuits

In this thesis the ANTICS analogue fault simulation software is described which provides a statistical approach to fault simulation for accurate analogue IC test evaluation. The traditional figure of fault coverage is replaced by the average probability of fault detection. This is later refined by considering the probability of fault occurrence to generate a more realistic, weighted test metric. Two techniques to reduce the fault simulation time are described, both of which show large reductions in simulation time with little loss of accuracy. The final section of the thesis presents an accurate comparison of three test techniques and an evaluation of dynamic supply current monitoring. An increase in fault detection for dynamic supply current monitoring is obtained by removing the DC component of the supply current prior to measurement

Quiescent current testing of CMOS data converters

Power supply quiescent current (IDDQ) testing has been very effective in VLSI circuits designed in CMOS processes detecting physical defects such as open and shorts and bridging defects. However, in sub-micron VLSI circuits, IDDQ is masked by the increased subthreshold (leakage) current of MOSFETs affecting the efficiency of I¬DDQ testing. In this work, an attempt has been made to perform robust IDDQ testing in presence of increased leakage current by suitably modifying some of the test methods normally used in industry. Digital CMOS integrated circuits have been tested successfully using IDDQ and IDDQ methods for physical defects. However, testing of analog circuits is still a problem due to variation in design from one specific application to other. The increased leakage current further complicates not only the design but also testing. Mixed-signal integrated circuits such as the data converters are even more difficult to test because both analog and digital functions are built on the same substrate. We have re-examined both IDDQ and IDDQ methods of testing digital CMOS VLSI circuits and added features to minimize the influence of leakage current. We have designed built-in current sensors (BICS) for on-chip testing of analog and mixed-signal integrated circuits. We have also combined quiescent current testing with oscillation and transient current test techniques to map large number of manufacturing defects on a chip. In testing, we have used a simple method of injecting faults simulating manufacturing defects invented in our VLSI research group. We present design and testing of analog and mixed-signal integrated circuits with on-chip BICS such as an operational amplifier, 12-bit charge scaling architecture based digital-to-analog converter (DAC), 12-bit recycling architecture based analog-to-digital converter (ADC) and operational amplifier with floating gate inputs. The designed circuits are fabricated in 0.5 μm and 1.5 μm n-well CMOS processes and tested. Experimentally observed results of the fabricated devices are compared with simulations from SPICE using MOS level 3 and BSIM3.1 model parameters for 1.5 μm and 0.5 μm n-well CMOS technologies, respectively. We have also explored the possibility of using noise in VLSI circuits for testing defects and present the method we have developed

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations