Elongated analytes are simple general-purpose model systems for nucleic acid strands, bacteriophages, nanoplastic fibers, nanotubes, nanorods, etc., and are characterized by numerous unknowns (e.g., material composition, length, orientation, etc.) that are difficult to measure accurately in real-time. This paper aims to advance the state-of-the-art of nanoscale sensing and metrology for these elongated, high aspect-ratio analytes, utilizing advanced data analysis methods specially developed for high-frequency capacitance spectroscopy measurements at nanoelectrode arrays. A model-based approach is proposed, integrating: (1) advanced supervised learning algorithms trained on an extensively augmented dataset derived from accurate physics-based numerical simulations; (2) a Markov Chain Monte Carlo (MCMC) Bayesian estimation framework for the parameters extraction task. The proposed algorithm achieves substantial speed enhancements while maintaining high accuracy, even at the resolution limits of the sensor. The test case is developed for multispectral capacitance images of 200-1000 nanometers long nanorods captured with an advanced 256 × 256 pixels nanocapacitor array. The proposed approach minimizes the need for time-consuming, physics-based simulations in sensor behavior prediction and Bayesian inference iterations. It is applicable to other elongated nanoscale analytes whose state is defined by many parameters. As a result, a robust and scalable solution for efficient and precise metrology of elongated analytes is established for high parallelism and high throughput nanocapacitor array sensor applications.

Open-world robotic tasks such as autonomous driving pose significant challenges to robot control due to unknown and unpredictable events that disrupt task performance. Neural network-based reinforcement learning (RL) techniques (like DQN, PPO, SAC, etc.) struggle to adapt in large domains and suffer from catastrophic forgetting. Hybrid planning and RL approaches have shown some promise in handling environmental changes but lack efficiency in accommodation speed. To address this limitation, we propose an enhanced hybrid system with a nested hierarchical action abstraction that can utilize previously acquired skills to effectively tackle unexpected novelties. We show that it can adapt faster and generalize better compared to state-of-the-art RL and hybrid approaches, significantly improving robustness when multiple environmental changes occur at the same time.

This paper presents an anisotropic adaptation strategy for three-dimensional unstructured tetrahedral meshes, which allows us to produce thin mostly anisotropic layers at the outside margin, i.e., the skin of an arbitrary meshed simulation domain. An essential task for any modern algorithm in the finite-element solution of partial differential equations, especially in the field of semiconductor process and device simulation, the major application is to provide appropriate resolution of the partial discretization mesh. The start-up conditions for semiconductor process and device simulations claim an initial mesh preparation that is performed by so-called Laplace refinement. The basic idea is to solve Laplace's equation on an initial coarse mesh with Dirichlet boundary conditions. Afterward, the gradient field is used to form an anisotropic metric that allows to refine the initial mesh based on tetrahedral bisection

The quality of the numeric approximation of the partial differential equations governing carrier transport in semiconductor devices depends particularly on the grid. The method of choice is to use structurally aligned grids since the regions and directions therein that determine device behavior are usually straightforward to find as they depend on the distribution of doping. Here, the authors present an algorithm for generating structurally aligned grids including anisotropy with resolutions varying over several orders of magnitude. The algorithm is based on a level set approach and permits to define the refined resolutions in a flexible manner as a function of doping. Furthermore, criteria on grid quality can be enforced. In order to show the practicability of this method, the authors study the examples of a trench gate metal-oxide-semiconductor field-effect transistor (TMOSFET) and a radio frequency silicon-on-insulator lateral double diffused metal-oxide-semiconductor (RF SOI LDMOS) power device using the device simulator MINIMOS NT, where simulations are performed on a grid generated by the new algorithm. In order to resolve the interesting regions of the TMOSFET and the RF SOI LDMOS power device accurately, several regions of refinement were defined where the grid was grown with varying resolutions.

We evaluate optimization techniques to reduce the necessary user interaction for inverse modeling applications as they are used in the technology computer-aided design field. Four optimization strategies are compared. Two well-known global optimization methods, simulated annealing and genetic optimization, a local gradient-based optimization strategy, and a combination of a local and a global method. We rate the applicability of each method in terms of the minimal achievable target value for a given number of simulation runs and in terms of the fastest convergence. A brief overview over the three used optimization algorithms is given. The optimization framework that is used to distribute the workload over a cluster of workstations is described. The actual comparison is achieved by means of an inverse modeling application that is performed for various settings of the optimization algorithms. All presented optimization algorithms are capable of evaluating several targets in parallel. The best optimization strategy that is found is used in the calibration of a model for silicon self-interstitial cluster formation and dissolution.

An algorithm for smoothing results of three-dimensional (3-D) Monte Carlo ion implantation simulations and translating them from the grid used for the Monte Carlo simulation to an arbitrary unstructured 3-D grid is presented. This algorithm is important for joining various process simulation steps, where data have to be smoothed or transferred from one grid to another. Furthermore, it is important for integrating the ion implantation simulator into a process flow. One reason for using different grids is that for certain Monte Carlo simulation methods, using orthogrids is mandatory because of performance reasons. The algorithm presented sweeps a small rectangular grid over the points of the new tetrahedral grid and uses approximation by generalized Bernstein polynomials. This approach was put on a mathematically sound basis by proving several properties of these polynomials. It does not suffer from the adverse effects of least squares fits of polynomials of fixed degree as known from the response surface method. The most important properties of Bernstein polynomials generalized to cuboid domains are presented, including uniform convergence, an asymptotic formula, and the variation diminishing property. The smoothing algorithm which works very fast is described and, in order to show its applicability, the resulting values of a 3-D real world implantation example are given and compared with those of a least squares fit of a multivariate polynomial of degree two, which yielded unusable results.

Filling high aspect ratio trenches is an essential manufacturing step for state of the art memory cells. Understanding and simulating the transport and surface processes enables one to achieve voidless filling of deep trenches, to predict the resulting profiles, and thus to optimize the process parameters and the resulting memory cells. Experiments on arsenic doped polysilicon deposition show that under certain process conditions step coverages greater than unity can be achieved. We developed a new model for the simulation of arsenic doped polysilicon deposition, which takes into account surface coverage dependent sticking coefficients and surface coverage dependent arsenic incorporation and desorption rates. The additional introduction of Langmuir-Hinshelwood type time dependent surface coverage enabled the reproduction of the bottom up filling of the trenches in simulations. Additionally, the rigorous treatment of the time dependent surface coverage allows to trace the in situ doping of the deposited film. The model presented was implemented and simulations were carried out for different process parameters. Very good agreement with experimental data was achieved with theoretically deduced parameters. Simulation results are shown and discussed for polysilicon deposition into 0.1 /spl mu/m wide and 7 /spl mu/m deep, high aspect ratio trenches.