Bertrand Rouet-Leduc

Nanocathodoluminescence Reveals Mitigation of the Stark Shift in InGaN Quantum Wells by Si Doping

Nanocathodoluminescence reveals the spectral properties of individual InGaN quantum wells in high efficiency light emitting diodes. We observe a variation in the emission wavelength of each quantum well, in correlation with the Si dopant concentration in the quantum barriers. This is reproduced by band profile simulations, which reveal the reduction of the Stark shift in the quantum wells by Si doping. We demonstrate nanocathodoluminescence is a powerful technique to optimize doping in optoelectronic devices.

Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning.

A fundamental challenge in the design of LEDs is to maximise electro-luminescence efficiency at high current densities. We simulate GaN-based LED structures that delay the onset of efficiency droop by spreading carrier concentrations evenly across the active region. Statistical analysis and machine learning effectively guide the selection of the next LED structure to be examined based upon its expected efficiency as well as model uncertainty. This active learning strategy rapidly constructs a model that predicts Poisson-Schrödinger simulations of devices, and that simultaneously produces structures with higher simulated efficiencies.

Spatial adaptive sampling in multiscale simulation

Abstract In a common approach to multiscale simulation, an incomplete set of macroscale equations must be supplemented with constitutive data provided by fine-scale simulation. Collecting statistics from these fine-scale simulations is typically the overwhelming computational cost. We reduce this cost by interpolating the results of fine-scale simulation over the spatial domain of the macro-solver. Unlike previous adaptive sampling strategies, we do not interpolate on the potentially very high dimensional space of inputs to the fine-scale simulation. Our approach is local in space and time, avoids the need for a central database, and is designed to parallelize well on large computer clusters. To demonstrate our method, we simulate one-dimensional elastodynamic shock propagation using the Heterogeneous Multiscale Method (HMM); we find that spatial adaptive sampling requires only ≈ 50 × N 0.14 fine-scale simulations to reconstruct the stress field at all N grid points. Related multiscale approaches, such as Equation Free methods, may also benefit from spatial adaptive sampling.

Distributed Database Kriging for Adaptive Sampling (D2KAS)

Abstract We present an adaptive sampling method supplemented by a distributed database and a prediction method for multiscale simulations using the Heterogeneous Multiscale Method. A finite-volume scheme integrates the macro-scale conservation laws for elastodynamics, which are closed by momentum and energy fluxes evaluated at the micro-scale. In the original approach, molecular dynamics (MD) simulations are launched for every macro-scale volume element. Our adaptive sampling scheme replaces a large fraction of costly micro-scale MD simulations with fast table lookup and prediction. The cloud database Redis provides the plain table lookup, and with locality aware hashing we gather input data for our prediction scheme. For the latter we use kriging, which estimates an unknown value and its uncertainty (error) at a specific location in parameter space by using weighted averages of the neighboring points. We find that our adaptive scheme significantly improves simulation performance by a factor of 2.5–25, while retaining high accuracy for various choices of the algorithm parameters.

Automatized convergence of optoelectronic simulations using active machine learning

A fundamental problem of optoelectronic simulations is to achieve convergence. We use statistical analysis and machine learning to effectively guide the selection of the next device to be examined based upon the expected convergence of the simulation. This active learning strategy rapidly constructs a model that predicts Poisson-Schrodinger simulations of devices and that simultaneously produces fully converged simulations.

The kinetics of heterogeneous nucleation and growth: an approach based on a grain explicit model

A model for phase transitions initiated on grain boundaries is proposed and tested against numerical simulations: this approach, based on a grain explicit model, allows us to consider the granular structure, resulting in accurate predictions for a wide span of nucleation processes. Comparisons are made with classical models of homogeneous (JMAK: Johnson and Mehl 1939 Trans. Am. Inst. Min. Eng. 135 416; Avrami 1939 J. Chem. Phys. 7 1103; Kolmogorov 1937 Bull. Acad. Sci. USSR, Mat. Ser. 1 335) as well as heterogeneous (Cahn 1996 Thermodynamics and Kinetics of Phase Transformations Im et al (Pittsburgh: Materials Research Society)) nucleation. A transition scale based on material properties is proposed, allowing us to discriminate between random and site-saturated regimes. Finally, we discuss the relationship between an Avrami-type exponent and the transition regime, establishing conditions for its extraction from experiments.