Single-Event Effects in Heavy-Ion Irradiated 3-kV SiC Charge-Balanced Power Devices
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Fragmentation Analysis of High-Energy Heavy Ion Beams for Single-Event Effects Testing at NSRL
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43 PARTICLE ACCELERATORS↗
Multiscale System Modeling of Single-Event-Induced Faults in Advanced Node Processors
Integration-technology feature shrink increases computing-system susceptibility to single-event effects (SEE). While modeling SEE faults will be critical, an integrated processor’s scope makes physically correct modeling computationally intractable. Without useful models, presilicon evaluation of fault-tolerance approaches becomes impossible. To incorporate accurate transistor-level effects at a system scope, we present a multiscale simulation framework. Charge collection at the 1) device level determines 2) circuit-level transient duration and state-upset likelihood. Circuit effects, in turn, impact 3) register-transfer-level architecture-state corruption visible at 4) the system level. Furthermore, the physically accurate effects of SEEs in large-scale systems, executed on a high-performance computing (HPC) simulator, could be used to drive cross-layer radiation hardening by design. We demonstrate the capabilities of this model with two case studies. First, we determine a D flip-flop’s sensitivity at the transistor level on 14-nm FinFet technology, validating the model against published cross sections. Second, we track and estimate faults in a microprocessor without interlocked pipelined stages (MIPS) processor for Adams 90% worst case environment in an isotropic space environment.
42 ENGINEERING↗
Reconfigurable Framework for Resilient Semantic Segmentation for Space Applications
Deep learning (DL) presents new opportunities for enabling spacecraft autonomy, onboard analysis, and intelligent applications for space missions. However, DL applications are computationally intensive and often infeasible to deploy on radiation-hardened (rad-hard) processors, which traditionally harness a fraction of the computational capability of their commercial-off-the-shelf counterparts. Commercial FPGAs and system-on-chips present numerous architectural advantages and provide the computation capabilities to enable onboard DL applications; however, these devices are highly susceptible to radiation-induced single-event effects (SEEs) that can degrade the dependability of DL applications. In this article, we propose Reconfigurable ConvNet (RECON), a reconfigurable acceleration framework for dependable, high-performance semantic segmentation for space applications. In RECON, we propose both selective and adaptive approaches to enable efficient SEE mitigation. In our selective approach, control-flow parts are selectively protected by triple-modular redundancy to minimize SEE-induced hangs, and in our adaptive approach, partial reconfiguration is used to adapt the mitigation of dataflow parts in response to a dynamic radiation environment. Combined, both approaches enable RECON to maximize system performability subject to mission availability constraints. We perform fault injection and neutron irradiation to observe the susceptibility of RECON and use dependability modeling to evaluate RECON in various orbital case studies to demonstrate a 1.5–3.0× performability improvement in both performance and energy efficiency compared to static approaches.
97 MATHEMATICS AND COMPUTING↗
Single-event burnout in homojunction GaN vertical PiN diodes with hybrid edge termination design
GaN devices play a major role in modern electronics, providing high-power handling, efficient high-frequency operation, and resilience in harsh environments. However, electric field crowding at the edge of the anode often limits its full potential, leading to single-event effects (SEEs) at lower bias voltages under heavy ion radiation. Here, in this work, we report on the performance of homojunction GaN vertical PiN diodes with a hybrid edge termination design under heavy ion irradiation, specifically, oxygen ions, chlorine ions, Cf-252 fission fragments, and alpha particles from an Am-241 source. The unique hybrid edge termination (HET) design provides better electric field management, preventing breakdown from occurring at the edge of the anode at lower voltages. The results of this study reveal that these devices exhibit excellent tolerance to 12-MeV oxygen and 16-MeV chlorine ions, owing to their low linear energy transfer (LET) and range in GaN. However, single-event burnout (SEB) is observed during the Cf-252 exposure at about 50% of the diodes' electrical breakdown voltage due to the presence of higher LET and longer-range ions. Optical and scanning electron microscopy (SEM) reveal that the damage that caused by SEB lies close to the center of these devices rather than the anode edge. Devices with junction termination extension (JTE) instead of HET edge termination also show similar SEB when irradiated with Cf-252 fission fragments. Physical damage due to SEB occurs at the edge of the anode for these devices. These comparative results show the benefits of HET for enhancing the resistance of GaN-based PiN diodes to heavy ion irradiation.
42 ENGINEERING↗
The Mu2e Digitizer ReAdout Controller (DiRAC): characterization and radiation hardness
The Mu2e experiment at Fermilab will search for the neutrino-less coherent conversion of a muon into an electron in the field of a nucleus. Mu2e detectors comprise a straw tracker, an electromagnetic calorimeter and a veto for cosmic rays. The calorimeter employs 1348 Cesium Iodide crystals readout by silicon photo-multipliers and fast front-end, and digitization electronics. The digitization board is named DiRAC (Digitizer ReAdout Controller) and 140 cards are needed for the readout of the full calorimeter. The DiRACs are hosted in crates located on the external surface of calorimeter disks, inside the detector solenoid cryostat and must sustain very high radiation and magnetic field so it was necessary to fully qualify it. Several version of prototypes were validated for operation in a high-vacuum (10−4 Torr) and under a 1T magnetic field. An extensive radiation hardness qualification campaign, carried out with photons, 14 MeV neutron beams, and 200 MeV protons, certified the DiRAC design to sustain doses up to 12 Krad, neutron fluences up to ∼ 1011 1 MeV neq/cm2, and very low occurrences of single-event effects. The qualification campaigns and quality assurance procedures will be reviewed.
43 PARTICLE ACCELERATORS↗
A Reconfigurable Neural Network ASIC for Detector Front-End Data Compression at the HL-LHC
Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network (NN) autoencoder model can be implemented in a radiation-tolerant application-specific integrated circuit (ASIC) to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile. For our application, we consider the high-granularity calorimeter from the Compact Muon Solenoid (CMS) experiment at the CERN Large Hadron Collider. The advantage of the machine learning approach is in the flexibility and configurability of the algorithm. By changing the NN weights, a unique data compression algorithm can be deployed for each sensor in different detector regions and changing detector or collider conditions. To meet area, performance, and power constraints, we perform quantization-aware training to create an optimized NN hardware implementation. The design is achieved through the use of high-level synthesis tools and the hls4ml framework and was processed through synthesis and physical layout flows based on a low-power (LP)-CMOS 65-nm technology node. The flow anticipates 200 Mrad of ionizing radiation to select gates and reports a total area of 3.6 mm 2 and consumes 95 mW of power. The simulated energy consumption per inference is 2.4 nJ. Furthermore, this is the first radiation-tolerant on-detector ASIC implementation of an NN that has been designed for particle physics applications.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Learning from many collider events at once
There have been a number of recent proposals to enhance the performance of machine learning strategies for collider physics by combining many distinct events into a single ensemble feature. To evaluate the efficacy of these proposals, we study the connection between single-event classifiers and multievent classifiers under the assumption that collider events are independent and identically distributed. We show how one can build optimal multievent classifiers from single-event classifiers, and we also show how to construct multievent classifiers such that they produce optimal single-event classifiers. This is illustrated for a Gaussian example as well as for classification tasks relevant for searches and measurements at the Large Hadron Collider. We extend our discussion to regression tasks by showing how they can be phrased in terms of parametrized classifiers. Empirically, we find that training a single-event (per-instance) classifier is more effective than training a multievent (per-ensemble) classifier, as least for the cases we studied, and we relate this fact to properties of the loss function gradient in the two cases. While we did not identify a clear benefit from using multievent classifiers in the collider context, we speculate on the potential value of these methods in cases involving only approximate independence, as relevant for jet substructure studies.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Impact of O4 future detections on the determination of the dense matter equations of state
Here, in view of the next LIGO-Virgo-KAGRA Observing period O4 (to start in Spring 2023), we address the question of the ability of the interferometers network to discriminate among different neutron stars equation of states better than what was possible with the observation of the binary neutron stars merger GW170817. We show that the observation of an event similar to GW170817 during O4 would allow us to resolve the dimensionless effective tidal deformability $\tilde{Λ}$ within an uncertainty 7 times better than the one obtained in O2. Thanks to the expected increase in sensitivities, we show that any GW170817-like single-event within a distance of 100 Mpc would imply significantly improved constraints of the neutron stars equations of state. We also illustrate the important impact of the noise in the analysis of the signal, showing how it can impact the effective tidal deformability probability density function for large signal-to-noise ratio.
79 ASTRONOMY AND ASTROPHYSICS↗
Charge Trap Layer Supercharging for Improved Bit Reliability in 3-D NAND Flash Under Proton Irradiation
Single-event upset (SEU) cross sections are reduced in 176-layer charge trap (CT) 3-D nand devices under proton irradiation when multiple write operations are applied sequentially without the typical erase-before-write. Here, this effect is observed for multiple data patterns and in both single-level cell (SLC) and triple-level cell (TLC) operating modes. SEU cross section calculation methodologies are discussed for highly scaled 3-D devices both with and without the application of rewrites, and potential implications for long-term endurance effects are proposed.
Breeding, Matthew Luel [Sandia National Laboratori↗
Prediction of Alpha-Particle-Immune Gate-All-Around Field-Effect Transistors (GAA-FET) Based SRAM Design
Alpha particles are known to be a major source of particles creating soft errors in semiconductor devices, such as content flipping in Static Random-Access Memory (SRAM). Recent advancements in transistor nodes have led to the introduction of Gate-All-Around Field Effect Transistors (GAA-FETs), which have better gate control, thus better electrostatics. Moreover, the introduction of bottom dielectric isolation (BDI) eliminates substrate leakage and thus is expected to enhance its radiation hardness. It is thus important to explore if one can design an SRAM that is completely radiation-hard to alpha particles. In this paper, using 3D Technology Computer-Aided-Design (TCAD) simulations, we show that it is possible to design an SRAM using GAA-FET technology so that it is immune to single alpha particle radiation error. In other words, with the design, there will be no single-event upset (SEU) due to alpha particles. We first use ab initio calculations in PHITS to show that there is a maximum linear energy transfer (LET), LET max , for the alpha particle in Si and Si x Ge 1-x . Based on that, by de signing a sub-7nm GAA-FET-based SRAM with BDI, we show that the SRAM does not flip even if the particle strike is in the worst-case scenario for LET > LET max .
42 ENGINEERING↗