An Efficient Approach for Soft Error Rate Estimation of Combinational Circuits

Nanometer integrated circuits are getting increasingly vulnerable to soft errors and making the soft error rate (SER) estimation an important challenge. In this study, a novel approach is proposed for SER estimation of combinational circuits based on vulnerability analysis. The authors introduce a concept called probabilistic vulnerability window (PVW) which is an inference of necessary conditions for a single event transient (SET) to cause observable errors in the circuit. A proposed computational framework calculates PVWs for all circuit gates in a backward‐traversing algorithm enabling the circuit designers for an accurate and efficient SER estimation. Experimental results show that the proposed approach is 2× faster than the traditional SER estimation methods and keep its efficiency when it is applied for estimating the SER considering various different SET widths while runtime of traditional estimation methods increases in such cases. In addition, results verify the accuracy (average difference of 0.02) and speedup (about four orders of magnitude) of the proposed method when compared with the Monte Carlo‐based fault injection simulation on ISCAS'85 benchmark circuits.

Nano-scale digital integrated circuits are getting increasingly vulnerable to soft errors due to aggressive technology scaling. On the other hand, the impacts of process variations on characteristics of the circuits in nano era make statistical approaches as an unavoidable option for soft error rate estimation procedure. In this paper, we present a novel statistical Soft Error Rate estimation framework. The vulnerability of the circuits to soft errors is analyzed using a newly defined concept called Statistical Vulnerability Window (SVW). SVW is an inference of the necessary conditions for a Single Event Transient (SET) to cause observable errors in the given circuit. The SER is calculated using a probabilistic formulation based on the parameters of SVWs. Experimental results show that the proposed method provides considerable speedup (about 5 orders of magnitude) with less than 5 % accuracy loss when compared to Monte-Carlo SPICE simulations. In addition, the proposed framework, keeps its efficiency when considering a full spectrum charge collections (more than 36X speedups compared to the most recently published similar work).

Recent advanced high-speed communication systems, such as optical systems, require highest reliability at lowest possible power consumption. Thus, Pass Transistor Logic (PTL) is gaining lots of interest in these communication systems due to its power saving potential compared to traditional CMOS logic. However, due to the non-conventional logic structure, its susceptibility to radiation-induced soft errors is different from CMOS circuitry. Due to the unique generation and propagation of Single Event Transients (SETs) in PTL, different approaches for PTL soft error rate (SER) estimation are required. In this paper we propose a machine learning (ML) approach for SET propagation in PTL logic. Multi-layer feed-forward neural network together with support vector classifier (SVC) are used to build the SET pulse width and pulse amplitude models. Bayesian optimization using Gaussian Processes is utilized to tune the hyperparameters of neural network. The experimental results on full adder (FA), which is the key component in many large cirucits such as ALU, and comparison with Monte Carlo (MC) spectre simulations confirm the accuracy and speed of the proposed method.

While circuit density and power efficiency increase with each major advance in IC technology, reliability with respect to soft errors tends to decrease. Current solutions to this problem such as TMR require high area and power overhead. In this work, soft-error reliability is improved with minimal area overhead by careful, localized circuit restructuring. The key idea is to increase logic masking of errors by taking advantage of conditions already present in the circuit, such as observability don't-cares. We describe two circuit modification techniques to improve reliability: don't-care-based resynthesis and local rewriting. A key feature of these techniques is fast, on-the-fly estimation of soft error rate (SER) using our reliability evaluator AnSER. This tool is compared against prior SER evaluators and found to run orders of magnitude faster. We show empirically that our reliability-driven synthesis methods can reduce SER by 29-40% with only 5-13% area overhead.

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

Soft error simulations utilizing PHYSERD are presented. The comparison with the experimental data for 28 nm SRAM demonstrates that PHYSERD provides the high-accuracy estimation of soft error rate when combined with circuit simulation, while PHYSERD alone tends to overestimate soft error rate. We also analyze the contributions of transistors constituting SRAM and of secondary ions to soft error rate. We find that circuit simulation has a significant effect on these contributions and is essential to the prediction of soft error rate.

Efficient algorithms to accurately compute derating factors of digital circuits

Single Event Transients (SETs) in clock-distribution networks are a major source of soft errors in synchronous systems. We present a practical framework that assesses SET risk early in the design cycle, before layout and parasitics, using a Vulnerability Function (VF) derived from Verilog fault injection. This framework guides targeted Engineering Change Orders (ECOs), such as clock-net remapping, re-routing, and the selective insertion of SET filters, within a reproducible open-source flow (Yosys, OpenROAD, OpenSTA). A new analytical Soft Error Rate (SER) model for clock trees is also proposed, which decomposes contributions from the root, intermediate levels, and leaves, and is calibrated by SPICE-measured propagation probabilities, area, and particle flux. When coupled with throughput, this model yields a frequency-aware system-level Bit Error Rate (BERsys). The methodology was validated on a First-In First-Out (FIFO) memory, demonstrating a significant vulnerability reduction of approximately 3.35× in READ mode and 2.67× in WRITE mode. Frequency sweeps show monotonic decreases in both clock-tree vulnerability and BERsys at higher clock frequencies, a trend attributed to temporal masking and throughput effects. Cross-node SPICE characterization between 65 nm and 28 nm reveals a technology-dependent effect: for the same injected charge, the 28 nm process produces a shorter root-level pulse, which lowers the propagation probability relative to 65 nm and shifts the optimal clock-tree partition. These findings underscore the framework’s key innovations: a technology-independent, early-stage VF for ranking critical clock nets; a clock-tree SER model calibrated by measured propagation probabilities; an ECO loop that converts VF insights into concrete hardening actions; and a fully reproducible open-source implementation. The paper’s scope is architectural and pre-layout, with extensions to broader circuit classes and a full electrical analysis outlined for future work.

As the technology scales down, space radiation induced soft errors are becoming a critical issue for the reliability of Integrated Circuits (ICs). In this paper, we propose a novel layout-based Single-Event Transient (SET) injection approach to evaluate the Soft Error Rate (SER) of large combinational circuits in Complementary Metal-Oxide-Semiconductor (CMOS) bulk technology. We consider the effect of ion strike location on the SET pulse width in this approach. Heavy-ion experiments on two different inverter chains are conducted to verify this layout-based SET injection approach. The simulation and experiment results show that this approach can fairly reflect the SET pulse width distribution. Furthermore, we compare the soft error number calculated by our proposed layout-based approach with the normal SET injection approach, and illustrate the detailed circuit response obtained by our proposed approach.

Characterizing, modeling, and analyzing soft error propagation in asynchronous and synchronous digital circuits

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.

Fast and accurate Soft Error Rate (SER) estimation is an important system design aspect. With the continued scaling of SRAM bit-cell dimensions and cell pitches, accurate modeling of the Multi-Bit Upset (MBU) component is becoming increasingly critical. This paper introduces MBU-Calc, a compact model for MBU SER estimation. The tool leverages Multi-Cell Upset (MCU) probabilities obtained through direct test-chip measurements. The main improvement with respect to prior published work [2] is the introduction of so-called Line Filling (LF) factors which enable more accurate projection and bucketing of silent data corruption (SDC) versus detected unrecoverable errors (DUE). The accuracy of the introduced model is demonstrated against measured MBU results.

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.

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.

Single event transients (SETs) stand out as one of the major causes of soft errors in nanoscale CMOS integrated circuits. To reduce the need for exhaustive circuit simulations in the design of radiation-hard integrated circuits, the cost-effective approaches for characterization and modeling of SET generation effects in standard logic cells are required. In this work, a SPICE-based methodology for characterization of SET generation effects, employing two different SET current models, is presented. Based on the acquired simulation results, the empirical models for the two main SET generation metrics (SET critical charge and SET pulse width) are derived. The SET generation models and the respective model parameters are intended to be used as inputs for the higher-level analysis of SET effects in digital circuits designed with the characterized standard logic cells. By storing the model parameters for each gate in the look-up table, instead of storing the raw data obtained from SPICE simulations, the amount of characterization data can be significantly reduced, allowing to speed up the subsequent SET analysis of a complex circuit.