The appearance of nanometer technologies has produced a significant increase of integrated circuit sensitivity to radiation, making the occurrence of soft errors much more frequent, not only in applications working in harsh environments, like aerospace circuits, but also for applications working at the earth surface. Therefore, hardened circuits are currently demanded in many applications where fault tolerance was not a concern in the very near past. To this purpose, efficient hardness evaluation solutions are required to deal with the increasing size and complexity of modern VLSI circuits. In this paper, a very fast and cost effective solution for SEU sensitivity evaluation is presented. The proposed approach uses FPGA emulation in an autonomous manner to fully exploit the FPGA emulation speed. Three different techniques to implement it are proposed and analyzed. Experimental results show that the proposed Autonomous Emulation approach can reach execution rates higher than one million faults per second, providing a performance improvement of two orders of magnitude with respect to previous approaches. These rates give way to consider very large fault injection campaigns that were not possible in the past

Estimation of soft error sensitivity is crucial in order to devise optimal mitigation solutions that can satisfy reliability requirements with reduced impact on area, performance, and power consumption. In particular, the estimation of Single Event Transient (SET) effects for complex systems that include a microprocessor is challenging, due to the huge potential number of different faults and effects that must be considered, and the delay-dependent nature of SET effects. In this paper, we propose a multilevel FPGA emulation-based fault injection approach for evaluation of SET effects called AMUSE (Autonomous MUltilevel emulation system for Soft Error evaluation). This approach integrates Gate level and Register-Transfer level models of the circuit under test in a FPGA and is able to switch to the appropriate model as needed during emulation. Fault injection is performed at the Gate level, which provides delay accuracy, while fault propagation across clock cycles is performed at the Register-Transfer level for higher performance. Experimental results demonstrate that AMUSE can emulate soft error effects for complex circuits including microprocessors and memories, considering the real delays of an ASIC technology, and support massive fault injection campaigns, in the order of tens of millions of faults within acceptable time.

Performance benchmarks have been used over the years to compare different systems. These benchmarks can be useful for researchers trying to determine how changes to the technology, architecture, or compiler affect the system's performance. No such standard exists for systems deployed into high radiation environments, making it difficult to assess whether changes in the fabrication process, circuitry, architecture, or software affect reliability or radiation sensitivity. In this paper, we propose a benchmark suite for high-reliability systems that is designed for field-programmable gate arrays and microprocessors. We describe the development process and report neutron test data for the hardware and software benchmarks.

Fault-tolerant circuits are currently required in several major application sectors, and a new generation of CAD tools is required to automate the insertion and validation of fault-tolerant mechanisms. This paper outlines the characteristics of a new fault-injection platform and its evaluation in a real industrial environment. The fault-injection platform is mainly used for assessing the correctness and effectiveness of the fault tolerance mechanisms implemented within ASIC and FPGA designs. The platform works on register transfer-level VHDL descriptions which are then synthesized, and is based on commercial tools for VHDL parsing and simulation. It also details techniques devised and implemented within the platform to speed-up fault-injection campaigns. Experimental results are provided, showing the effects of the different techniques, and demonstrating that they are able to reduce the total time required by fault-injection campaigns by at least one order of magnitude.

This paper proposes a soft error characterization methodology to analyze multiple faults caused by single-event-induced charge sharing in standard-cell based ASIC designs. Fault injection campaigns have been executed using data provided by placement analysis as well as a pulse width modeling through electrical simulation. Experimental results demonstrate that the error rate can be largely overestimated if placement is not considered.