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Multiscale System Modeling of Single-Event-Induced Faults in Advanced Node Processors

IEEE Transactions on Nuclear Science

Cannon, Matthew J.; Rodrigues, Arun; Black, Dolores A.; Black, Jeff; Bustamante, Luis; Feinberg, Benjamin M.; Quinn, Heather M.; Clark, Lawrence T.; Brunhaver, John S.; Barnaby, Hugh; Mclain, Michael; Agarwal, Sapan; Marinella, Matthew

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. Thus, 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.

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TYPE Journal Article YEAR 2021
DOIOSTIScopus

Simulation of powder bed metal additive manufacturing microstructures with coupled finite difference-Monte Carlo method

Additive Manufacturing

Rodgers, Theron M.; Abdeljawad, Fadi; Moser, Daniel R.; Bays, Nathan R.; Carroll, J.D.; Jared, Bradley H.; Bolintineanu, Dan S.; Mitchell, John A.; Madison, Jonathan D.

Grain-scale microstructure evolution during additive manufacturing is a complex physical process. As with traditional solidification methods of material processing (e.g. casting and welding), microstructural properties are highly dependent on the solidification conditions involved. Additive manufacturing processes however, incorporate additional complexity such as remelting, and solid-state evolution caused by subsequent heat source passes and by holding the entire build at moderately high temperatures during a build. We present a three-dimensional model that simulates both solidification and solid-state evolution phenomena using stochastic Monte Carlo and Potts Monte Carlo methods. The model also incorporates a finite-difference based thermal conduction solver to create a fully integrated microstructural prediction tool. The three modeling methods and their coupling are described and demonstrated for a model study of laser powder-bed fusion of 300-series stainless steel. The investigation demonstrates a novel correlation between the mean number of remelting cycles experienced during a build, and the resulting columnar grain sizes.

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TYPE Journal Article YEAR 2021
DOIOSTIScopus
Results 651–675 of 9,998
Results 651–675 of 9,998
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