A Detailed Methodology to Compute Soft Error Rates in Advanced Technologies

With the advances of semiconductor technology, the transistor size has been scaled down. This shrinkage makes the circuits more susceptible to the soft error, which is a wrong signal or data latched by storage elements. Thus, Soft Error Rate (SER) reduction has become an important issue recently. In this work, we propose two methods for minimizing this SER based on a circuit restructuring technique, IRredundancy Removal and Addition. Our first method is to rewire the higher SER gates while the second one is to restructure the subcircuits having higher soft error impacts. Experiential results show that the first method presents a reduction of 19.6\% SER and 9.9\% area on a set of ISCAS'85 and MCNC benchmarks on average. The second method gets 23.7\% soft error rate reduction with 9.6\% area overhead. By combining these two methods, we can rise the ratio of SER decreasing rate to area increasing rate up to 6.77. This ratio indicates our approach can reduce the SER within a small cost.

Summary form only given. We consider the soft error sensitivity trends to various memory and logic components as they are scaled to smaller dimensions, higher integration densities, and lower operating voltages. We also review the three radiation mechanisms responsible for soft errors in the terrestrial environment and discuss the methods for characterizing radiation sensitivity and methods for extrapolating product soft error rate (SER) from accelerated tests - with a focus on the difficulties in using test chip SER data to derive actual product mean-time-to-failure from soft errors. We then focus on technology scaling trends for SER, showing that although DRAM bit SER has been reduced by about four to five times per generation, DRAM system failure rates remain unchanged because the amount of system memory has increased as fast as the reductions in DRAM bit SER. We also show that in deep sub-micron regime, the SRAM single bit SER saturates as a function of technology scaling. We also review techniques for reducing SER and conclude that error correction is the beast means of mitigating memory soft errors, and that in high reliability systems that employ error correction on all embedded memory, the product failure rate is limited by the sequential logic SER.

Soft error reliability is increasingly becoming a first-order design concern for microprocessors, as a result of higher transistor counts, shrinking device geometries and lowering of operating voltages. It is important for designers to be able to validate whether the Soft Error Rate (SER) targets of their design have been met, and help end users select the processor best suited to their reliability goals. The knowledge of the observable worst-case SER allows designers to select their design point, and bound the worst-case vulnerability at that design point. We highlight the lack of a methodology for evaluation of the overall observable worst-case SER. Hence, there is a clear need for a so called stress mark that can demonstrably approach the observable worst-case SER. The worst-case thus obtained can be used to identify reliability bottlenecks, validate safety margins used for reliability design and identify inadequacies in benchmark suites used to evaluate SER. Starting from a comprehensive study about how micro architecture-dependent program characteristics affect soft errors, we derive the insights needed to develop an automated and flexible methodology for generating a stress mark that approaches the maximum SER of an out-of-order processor. We demonstrate how our methodology enables architects to quantify the impact of SER-mitigation mechanisms on the worst-case SER of the processor. The stress mark achieves 1.4X higher SER in the core, 2.5X higher SER in DL1 and DTLB, and 1.5X higher SER in L2 as compared to the highest SER induced by SPEC CPU2006 and MiBench programs.

Efficient algorithms to accurately compute derating factors of digital circuits

Describes the alpha-particle-induced soft errors in ECL logic circuitry. Soft-error 'hot' testing has been performed using 2.5-, 1.7-, and 1.3- mu m ECL gate arrays exposed to /sup 241/Am. Experimental results reveal that: (1) the soft errors in ECL logic circuitry are caused by two types of upsets, latch toggling and gate upsets in clock lines; (2) the soft error rate (SER) increases rapidly as feature size is scaled down; and (3) the SER increases rapidly as switching current is reduced. To overcome the degradation of soft-error tolerance, a soft-error hardened (SEH) circuit design technique employing a new driver configuration which prevents upsets of critical nodes from propagating to following gates is introduced. The SEH design has been implemented in the ECL gate arrays and verified to improve the SER by more than 10/sup 2/ times as compared with conventional circuits. >

An accurate model for soft error rate estimation considering dynamic voltage and frequency scaling effects

Single Event Upsets (SEUs), bit flips caused when a high-energy particle strikes a sensitive node in a microelectronic device, are an increasing problem with advances in technologies. With submicron devices available today, electronics are vulnerable to SEUs at both space-based and avionic altitudes. This paper focuses on SEUs in avionic systems induced by atmospheric neutrons; atmospheric neutrons are produced in nuclear reactions of cosmic rays reacting with air molecules. The soft error rate (SER) of CMOS device models due to atmospheric neutrons for deep submicron technologies were computed at various altitudes and latitudes using Total Space and Atmospheric Effects on Microelectronics (TSAREME) developed at Boeing. Model predictions of the SER for 350 and 600 nm CMOS representative of 1992 and 1994 were 1.8 and 3 upsets per day, respectively. Predictions of 9.8 and 11.6 upsets per day were made for 100 and 50 nm CMOS devices representing the technologies of today. These observations taken together indicated that the SER increased with decreasing feature size at a rate less than linear for sizes below 100 nm. This linear trend of the SER with feature size is in agreement with empirical and semi-empirical observations. The paper also indicated that materials commonly used to fabricate chips such as borophosilicate glass (BPSG) and shielding by high Z metals such as tantalum significantly increased the SER and so should be avoided. The results all indicate that atmospheric neutrons are an important consideration in determining the radiation hardness of avionic systems with very-large-scale integration (VLSI) units.

This paper presents measurement results of alpha-particle-induced soft errors and multiple cell upsets (MCUs) in 65-nm 10 T SRAM with a wide range of supply voltage from 1.0 V to 0.3 V. We reveal that the soft error rate (SER) at 0.3 V is eight times higher than SER at 1.0 V, and the ratio of MCUs to the total upsets increases as the supply voltage decreases. The SER and ratio of MCUs with body-biasing are also described. In addition, we investigate an impact of manufacturing variability on the soft error immunity of each memory cell. In our measurement, a distinct influence of manufacturing variability is not observed even in subthreshold region.

Accurate characterization of radiation-induced soft errors is a critical step toward understanding the impact of these glitches on circuit and system reliability. With process scaling, there has been exponential increase in number of transistors that can be packed on a die which, in turn, results in higher sensitive node count and persistent soft error susceptibilities. In this work, a novel circuit technique employing higher sensitivity toward soft errors is proposed. The circuit makes use of current-starved gates with bias knobs to fine-tune both measurement resolution and strike sensitivity enabling accelerated and efficient induction of errors in a limited-time irradiation test environment. The back-sampling chain (BSC) circuit can measure individual radiation-induced transient pulse with as low amplitude as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.3\times $ </tex-math></inline-formula> VDD while maintaining a high measurement resolution for pulsewidth characterization. The bias knobs allowing tuning of sensitivity and resolution enable, for the first time, a strike pulse waveform reconstruction methodology that can be used to calibrate current pulse models for assessing soft error rate (SER) sensitivity of standard logic gates.

As CMOS process technology scales below 100nm, the amount of charge required to upset a gate or memory cell (Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">crit</sub> ) is decreasing. Therefore, the probability that an energetic particle can generate enough charge to upset a circuit is increasing. Since these single event upsets do not damage the IC, they are called soft errors. With the proper detection and correction schemes, these particle induced soft errors can be mitigated in a way that does not impact the overall reliability of an electronic system. Since memory devices such as SRAM, DRAM and Flash comprise the largest gate counts in many designs, it is essential to understand their soft error mechanisms, characterize their soft error rates and develop effective mitigation techniques.

Accelerated soft error testing with proton beam was performed for 65 nm CMOS latches for the first time. The soft-error rate (SER) dependence on the physical layout was clarified. SER has dependence on the size of the diffusion regions since critical charge and charge collection is strong function of them. By optimizing it, SER can be reduced by 70%. The scaling trend of SER was also investigated. It is shown that SER degradation due to scaling can be suppressed by the moderate reduction of the supply voltage.

A soft error rate analysis model for MOS dynamic RAM's is presented. The soft error rate can be quantitatively calculated by using a solution of the equations for diffusion and collection of alpha-particle-induced excess electrons and by combining a statistical treatment of alpha particle energy, incidence angles, and incidence positions with the noise charge calculation. The model is then applied to analyze a soft error experiment on 64-kbit dynamic RAM's. It is shown that soft error characteristics with regard to signal charge (critical charge), as well as alpha energy and incidence angle dependencies, can be definitely determined. The model can also be used to predict the location of soft errors in MOS dynamic RAM's.

In this paper, the Am-241 is used as an alpha particle radioactive source to investigate the soft error mechanism in 65-nm and 90-nm static random accessmemory (SRAM). Combining reverse analysis, TRIM and CREME-MC Monte Carlo simulation, the energy transport process, deposited energy spectrum and cross-section characteristics of alpha particles in the device are revealed. The results show that the soft error sensitivity of the 65-nm device is much higher than that of the 90-nm device, and no flipping polarity is found. According to the real-time measured soft error rate at an altitude of 4300 m in Tibetan Yangbajing area, the thermal neutron sensitivity and alpha particle soft error rate, the overall soft error rate of 65-nm SRAM used at sea level of Beijing city is 429 FIT/Mb, and the contribution from alpha particles is 70.63%. Based on the results of reverse analysis, a three-dimensional simulation model of the device is constructed to study the influence of the incident angle of alpha particles on the single event upset characteristics. It is found that the corresponding deposition energy value at the peak of the number of particles in the sensitive region decreases by 40% with the incident angle increasing from 0° to 60°. While the single event upset cross sectionincreases by 79% due to the apparent single event upset edge effect in a sensitive region of the 65-nm device.

The trends in technology scaling have made nanometer designs highly susceptible to reliability threats like soft errors and crosstalk noise while uncertainty in process parameters have made the physical realization of devices and interconnects unpredictable. The limitations in manufacturing processes and the impact of environmental noise poses a major threat to the signal quality, and hence the realization of reliable, low-power, high performance designs with acceptable parametric yields is a challenging problem. Most noise analysis and prevention techniques reported in the literature target single noise sources. However, reliability issues like crosstalk noise and radiation induced soft errors are deeply inter-related. Further, manufacturing variations have decreased the efficacy of online detection and correction schemes used for traditional noise optimization. In this work, we have proposed a methodology for simultaneous optimization of soft error rate(SER), crosstalk noise and power of circuits with delay constraints under process variations. Soft errors are modeled using a novel first order analysis model while crosstalk noise is modeled at the logic level using clustering based on Rent's exponent. The proposed multi-metric gate sizing methodology have been formulated into a mathematical program and has been efficiently solved. Experimental results on ISCAS'85 benchmark circuits indicate significant improvements in SER, crosstalk noise and timing yield compared to the corresponding constrained optimization problems.

Soft error rate (SER) testing and measurements of semiconductor circuits with different operating voltages and operating conditions have been performed using the thermal neutron beam at the Radiation Science and Engineering Center (RSEC) at Penn State University. The high neutron flux allows for accelerated testing for SER by increasing reaction rate densities inside the tested device that gives more precision in the experimental data with lower experimental run time. The effect of different operating voltages and operating conditions on INTEL PXA270 processor has been experimentally determined. Experimental results showed that the main failure mechanism was the segmentation faults in the system. Failure response of the system to the operating conditions was in agreement with the general behavior of SERs.