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Results for “modeling semiconductor manufacturing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Simulating Energy and Security Interactions in Semiconductor Manufacturing: Insights from the Intel Minifab Model

Semiconductor manufacturing is a highly complex. Fabrication plants must deal with re-entrant flows to support multiple types of wafers being produced simultaneously, each with their own deadlines and specifications. The manufacturing process itself depends upon the ability to control and programmatically adjust a variety of environmental conditions including temperature, humidity, and air quality. In addition, wafer fabrication consumes large amounts of electricity. Emerging technologies may help reduce the energy footprint of such facilities but can introduce cybersecurity risks. Therefore, this paper presents a modeling and simulation framework to quantify tradeoffs between operational measures of performance, energy consumption, and cybersecurity controls. We augment the Intel Minifab model, with the Purdue Enterprise Reference Architecture (PERA) for cybersecurity as well as tool-level energy consumption data from a real-world semiconductor manufacturing testbed. In this manner, we intend to provide stakeholders with a systematic, data-driven approach to evaluate emerging risks within the manufacturing process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Transformer coupled toroidal wave-heated remote plasma sources operating in Ar/NF 3 mixtures

Remote plasmas are used in semiconductor device manufacturing as sources of radicals for chamber cleaning and isotropic etching. In these applications, large fluxes of neutral radicals (e.g. F, O, Cl, H) are desired with there being negligible fluxes of potentially damaging ions and photons. One remote plasma source (RPS) design employs toroidal, transformer coupling using ferrite cores to dissociate high flows of moderately high pressure (up to several Torr) electronegative gases. In this paper, results are discussed from a computational investigation of moderate pressure, toroidal transformer coupled RPS sustained in Ar and Ar/NF 3 mixtures. Operation of the RPS in 1 Torr (133 Pa) of argon with a power of 1.0 kW at 0.5 MHz and a single core produces a continuous toroidal plasma loop with current continuity being maintained dominantly by conduction current. Operation with dual cores introduces azimuthal asymmetries with local maxima in plasma density. Current continuity is maintained by a mix of conduction and displacement current. Operation in NF 3 for the same conditions produces essentially complete NF 3 dissociation. Electron depletion as a result of dissociative attachment of NF 3 and NF x fragments significantly alters the discharge topology, confining the electron density to the downstream portion of the source where the NFx density has been lowered by this dissociation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Applications of Federated Learning in Semiconductor Manufacturing [Poster]

As semiconductor manufacturers explore advanced data analytics and modeling techniques and data hungry machine learning models increase in popularity due to their accuracy in solving generalized problems and ability to learn complex relationships, federated learning emerges as a privacy preserving machine learning technique for preserving data privacy and ensuring intellectual property protection. Federated Learning is a machine learning technique focused on training models using distributed data that never needs to be centrally stored, allowing the use of advanced machine learning techniques without compromising data privacy, and in the semiconductor manufacturing industry advanced machine learning techniques can reduce cost and time, but maintaining data privacy is essential to maintaining a competitive advantage. This paper systematically reviews existing literature on applications of federated learning in the semiconductor manufacturing industry with a focus on identifying common themes, algorithms, and gaps within the literature to drive future research directions. The findings reveal five key themes, including improvements in quality assurance, virtual models, privacy preservation, reliable data practices, and emerging trends and developments. By identifying key themes in literature on federated learning and semiconductor manufacturing and analyzing gaps and discussed methodologies, this study highlights several potential future research directions to expand the application of federated learning techniques in the semiconductor manufacturing domain.

42 ENGINEERING↗
NASA NTRS1990

Investigation of ultra wideband multi-channel dichroic beamsplitters from 0.3 to 52 microns

The development of a set of multi-channel dichroics which includes a 6 channel dichroic operating over the wavelength region from 0.3 to 52 microns is described. In order to achieve the optimum performance, the optical constraints of PbTe, Ge, and CdTe coatings in the strongly absorptive region have been determined by use of a new iterative method using normal incidence reflectance measurement of the multilayer together with initial values of energy gap and infinite refractive index for the semiconductor model. The design and manufacture of the dichroics is discussed and the final results are presented.

Zhang, K. Q.↗
DOE OSTI2024

Design, tuning, and blackbox optimization of laser systems

Chirped pulse amplification (CPA) and subsequent nonlinear optical (NLO) systems constitute the backbone of myriad advancements in semiconductor manufacturing, communications, biology, defense, and beyond. Accurately and efficiently modeling CPA+NLO-based laser systems is challenging because of the complex coupled processes and diverse simulation frameworks. Our modular start-to-end model unlocks the potential for exciting new optimization and inverse design approaches reliant on data-driven machine learning methods, providing a means to create tailored CPA+NLO systems unattainable with current models. To demonstrate this new, to our knowledge, technical capability, we present a study on the LCLS-II photo-injector laser, representative of a high-power and spectro-temporally non-trivial CPA+NLO system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Simulating Properties of Metal Powder Beds Used for Additive Manufacturing of Parts in Semiconductor, Solar and Display Equipment. Final CRADA Report

Final Report CRADA No. TC02261 Date Technical Work Ended: 7/31/19. This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Applied Materials, Inc. to adapt and utilize LLNL-developed particle-scale simulation models to investigate and improve powder-bed formation for additive manufacturing of parts of interest to Participant in semiconductor, solar and display equipment. The original term of the CRADA was one (1) year. A No-Cost Time Extension extended the CRADA for two months through July 31, 2019.

14 SOLAR ENERGY↗
NASA NTRS1999

Diagnostic for Plasma Enhanced Chemical Vapor Deposition and Etch Systems

In order to meet NASA's requirements for the rapid development and validation of future generation electronic devices as well as associated materials and processes, enabling technologies ion the processing of semiconductor materials arising from understanding etch chemistries are being developed through a research collaboration between Stanford University and NASA-Ames Research Center, Although a great deal of laboratory-scale research has been performed on many of materials processing plasmas, little is known about the gas-phase and surface chemical reactions that are critical in many etch and deposition processes, and how these reactions are influenced by the variation in operating conditions. In addition, many plasma-based processes suffer from stability and reliability problems leading to a compromise in performance and a potentially increased cost for the semiconductor manufacturing industry. Such a lack of understanding has hindered the development of process models that can aid in the scaling and improvement of plasma etch and deposition systems. The research described involves the study of plasmas used in semiconductor processes. An inductively coupled plasma (ICP) source in place of the standard upper electrode assembly of the Gaseous Electronics Conference (GEC) radio-frequency (RF) Reference Cell is used to investigate the discharge characteristics and chemistries. This ICP source generates plasmas with higher electron densities (approximately 10(exp 12)/cu cm) and lower operating pressures (approximately 7 mTorr) than obtainable with the original parallel-plate version of the GEC Cell. This expanded operating regime is more relevant to new generations of industrial plasma systems being used by the microelectronics industry. The motivation for this study is to develop an understanding of the physical phenomena involved in plasma processing and to measure much needed fundamental parameters, such as gas-phase and surface reaction rates. species concentration, temperature, ion energy distribution, and electron number density. A wide variety of diagnostic techniques are under development through this consortium grant to measure these parameters. including molecular beam mass spectrometry (MBMS). Fourier transform infrared (FTIR) spectroscopy, broadband ultraviolet (UV) absorption spectroscopy, a compensated Langmuir probe. Additional diagnostics. Such as microwave interferometry and microwave absorption for measurements of plasma density and radical concentrations are also planned.

Cappelli, Mark A.↗
DOE OSTI2020

Improving Gas Reactor Design with Complex Non-Standard Reaction Mechanisms in a Reactive Flow Model

Fluorinated gases are critical to a number of high-technology industries including semiconductor manufacturing and related processes such as flat panel display, LED, and photovoltaic manufacturing. These fluorinated gases have very high global warming potentials (GWPs) and, unfortunately are chemically very stable, in some cases lasting for thousands of years in the atmosphere. For environmental reasons, it is imperative that the release of these gases be minimized, but existing methods to destroy them requires a significant amount of energy. The goal of this project was to develop a more energy efficient method of destroying these high GWP gases using computational fluid dynamics, but the computational requirements are significant. In this project, we bring together resources available at the national labs, including high-performance computing hardware and modern computational methods, in order to explore an industrially important problem with key environmental and economic impact. In this project, we examined the state-of-the-art models for the physical processes involved, and made subtle changes to arrive at alternative formulations considerably better suited for computation and study. We examined several representative scenarios and developed a set of analyses techniques and tools that could be used to explore the systems in much greater detail. This exploratory project has demonstrated to ALZETA that HPC can indeed provide a unique view into their complex engineering problem - one that is simply inaccessible by any other means. The tools allow us to explore the details of the chemical process, and allow one to construct "what-if" scenarios to address shortcomings of the physical device in its current operating modes. However, as with many sources of completely new information, many new questions are illuminated with this capability. It will take considerable time, effort and experience before we learn to probe the solutions, pose operating scenarios and confirm modifications to the device that lead to real engineering advances.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Evaluation of Converter Performance Considering Static and Dynamic Device Part-to-Part Variability

This paper presents a methodology to incorporate and analyze the impact of semiconductor device part-to-part variation on power converter performance. By integrating extensive static and dynamic device characterization data with an automated compact model generation process that reflects manufacturing variability, device models with inherent variability features are utilized in converter simulations for a comprehensive assessment of performance impacts. The traditional converter performance evaluation process typically yields fixed efficiency values, often dismissing the inherent part-to-part variability caused by the manufacturing process of semiconductor devices. To address this limitation, a large population of devices was characterized to capture variations in static parameters-such as transfer, output, and capacitance characteristics-as well as dynamic behaviors, including switching losses. This data-driven approach enables the development of individual compact models, which were then integrated into converter simulations to evaluate efficiency ranges rather than single point estimated values. The converter simulation results show that part-to-part component variation can lead to significant efficiency deviations, exceeding several percentage points in high-power conversion applications. By offering a more accurate representation of converter behavior under real-world manufacturing conditions, this methodology enables designers to anticipate performance variability, improving the robustness of power converter designs.

device characterization↗
NASA NTRS1997

Etch Profile Simulation Using Level Set Methods

Etching and deposition of materials are critical steps in semiconductor processing for device manufacturing. Both etching and deposition may have isotropic and anisotropic components, due to directional sputtering and redeposition of materials, for example. Previous attempts at modeling profile evolution have used so-called "string theory" to simulate the moving solid-gas interface between the semiconductor and the plasma. One complication of this method is that extensive de-looping schemes are required at the profile corners. We will present a 2D profile evolution simulation using level set theory to model the surface. (1) By embedding the location of the interface in a field variable, the need for de-looping schemes is eliminated and profile corners are more accurately modeled. This level set profile evolution model will calculate both isotropic and anisotropic etch and deposition rates of a substrate in low pressure (10s mTorr) plasmas, considering the incident ion energy angular distribution functions and neutral fluxes. We will present etching profiles of Si substrates in Ar/Cl2 discharges for various incident ion energies and trench geometries.

Hwang, Helen H.↗
DOE OSTI2025

A Data-Agnostic, Continuous Machine Learning Framework for Application in High Energy Physics and Beyond: Phase 1 Final Scientific/Technical Report

This Phase 1 effort has focused on the development of continual learning frameworks for use in machine learning, specifically in the applied context of High Energy Physics (HEP). Machine learning (ML) is a transformative technology by which computers, typically through the use of neural networks, are able to perform tasks with proficiency that rivals or surpasses that of human users. Model Degradation & Catastrophic Forgetting are two undesired phenomena which can occur in ML where the performance of a model degrades when either deployed on novel data streams, or trained on novel data which are sufficiently different than the data the models were initially trained on. A natural example where these sorts of effects can be observed is in the performance of detectors in harsh environments, where the detector signature may change over the lifetime of the detector as it ages and deteriorates — precisely what occurs in the experiments conducted in HEP. Real world HEP data is therefore an excellent test-ground and use-case for Continual Learning paradigms, which are techniques used in ML to counteract these problems. Ensemble learning is one such technique, where multiple smaller models are trained on subsets of the overall data and are ensembled together during inference. The intuition behind this technique is that, although there are shifts in the distributions which govern the incoming data streams, these shifts are not expected to be homogeneous or global. If a sufficient diversity in solutions within the various sub-models has been achieved, then at least one sub-model is expected to retain its performance within the overall ensemble. One further strength of this approach is that the architectures of the various models do not need to be identical, and in fact even different modalities of data can naturally be combined in this way. This work focused on applying ensemble learning techniques to derive results using two main datasets, anomaly detection in HEP data & time-series forecasting in semiconductor manufacturing data. Semiconductor manufacturing involves data with surprising similarity to that of HEP (e.g. wafer maps look very similar to digi-occupancy maps) and Cerium Lab’s prominence within the semiconductor industry makes semiconductor manufacturing a natural opportunity for commercialization of this work. Our efforts have led to two strong results. The first is that we evaluated the proposed ensembling techniques using previously proposed machine learning architectures for use in anomaly detection, namely AutoEncoder based models and their derivatives. We also developed new architectures which have not been evaluated in this context before. In fact, this work marks the first use of Vision Transformers for anomaly detection in HEP. Second, we demonstrated that ensemble learning significantly improves model performance in scenarios prone to degradation, validating its effectiveness across both HEP and semiconductor datasets. These results further support ensemble learning as a powerful strategy for mitigating catastrophic forgetting and maintaining robust performance in evolving data environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Exploring Flood Predictability in Taiwan through Coupled Atmospheric–Hydrological and High-Performance Hydrodynamic Models

Effective flood simulation capabilities can tremendously support early warning and disaster prevention. To examine the applicability of a fully physics-based and high-performance flood simulation and forecasting modeling framework for a flood-prone region in Taiwan, we conduct a numerical experiment that couples the Weather Research and Forecasting (WRF) Model, WRF-Hydrological modeling system (WRF-Hydro), and the Two-Dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) to perform integrated rainfall, streamflow, and flood simulations. Furthermore, we first use the coupled WRF and WRF-Hydro (WWH) to predict rainfall and streamflow and then drive TRITON with the predicted streamflow hydrographs to simulate flood depth and inundation area. With the refined spatial resolution and parameterization, this framework can better predict rainfall with reasonable spatial patterns. Although WWH could overestimate the amount of rainfall in some areas, the uncertain rainfall–streamflow predictions produce reasonable flood maps able to pinpoint regions at risk of flooding. In terms of model efficiency, the graphics processing unit–based computation can yield a speed-up factor as high as ∼13 compared to the central processing unit–based computation, promoting the efficacy of the coupled modeling framework in practical real-time flood forecasting.

Coupled models↗
NASA NTRSDate not supplied

Technology Assessment and Modelling of Three Terminal Tandem Solar Cells for In-Space Utilization

NASA seeks a sustained human presence on the lunar surface. High-efficiency and resilient energy systems are critical to supporting habitats, scientific outposts, and lunar surface operations. Photovoltaics have long been the backbone of space power systems, transitioning from silicon solar cells to multi-junction III-V solar cells, which now dominate state-of-the-art (SOA) technology. Multi-junction cells achieve high efficiency by layering semiconductors, each absorbing a specific portion of the solar spectrum. However, efficiency gains are increasingly constrained by device physics and manufacturing complexity. Additionally, higher-order junctions pose challenges for accurate modelling and performance characterization due to difficulties in replicating the AM0 spectrum.

solar cells↗
NASA NTRS1995

High Performance Parallel Computational Nanotechnology

At a recent press conference, NASA Administrator Dan Goldin encouraged NASA Ames Research Center to take a lead role in promoting research and development of advanced, high-performance computer technology, including nanotechnology. Manufacturers of leading-edge microprocessors currently perform large-scale simulations in the design and verification of semiconductor devices and microprocessors. Recently, the need for this intensive simulation and modeling analysis has greatly increased, due in part to the ever-increasing complexity of these devices, as well as the lessons of experiences such as the Pentium fiasco. Simulation, modeling, testing, and validation will be even more important for designing molecular computers because of the complex specification of millions of atoms, thousands of assembly steps, as well as the simulation and modeling needed to ensure reliable, robust and efficient fabrication of the molecular devices. The software for this capacity does not exist today, but it can be extrapolated from the software currently used in molecular modeling for other applications: semi-empirical methods, ab initio methods, self-consistent field methods, Hartree-Fock methods, molecular mechanics; and simulation methods for diamondoid structures. In as much as it seems clear that the application of such methods in nanotechnology will require powerful, highly powerful systems, this talk will discuss techniques and issues for performing these types of computations on parallel systems. We will describe system design issues (memory, I/O, mass storage, operating system requirements, special user interface issues, interconnects, bandwidths, and programming languages) involved in parallel methods for scalable classical, semiclassical, quantum, molecular mechanics, and continuum models; molecular nanotechnology computer-aided designs (NanoCAD) techniques; visualization using virtual reality techniques of structural models and assembly sequences; software required to control mini robotic manipulators for positional control; scalable numerical algorithms for reliability, verifications and testability. There appears no fundamental obstacle to simulating molecular compilers and molecular computers on high performance parallel computers, just as the Boeing 777 was simulated on a computer before manufacturing it.

Saini, Subhash↗
NASA NTRS2010

Scaled CMOS Technology Reliability Users Guide

The desire to assess the reliability of emerging scaled microelectronics technologies through faster reliability trials and more accurate acceleration models is the precursor for further research and experimentation in this relevant field. The effect of semiconductor scaling on microelectronics product reliability is an important aspect to the high reliability application user. From the perspective of a customer or user, who in many cases must deal with very limited, if any, manufacturer's reliability data to assess the product for a highly-reliable application, product-level testing is critical in the characterization and reliability assessment of advanced nanometer semiconductor scaling effects on microelectronics reliability. A methodology on how to accomplish this and techniques for deriving the expected product-level reliability on commercial memory products are provided.Competing mechanism theory and the multiple failure mechanism model are applied to the experimental results of scaled SDRAM products. Accelerated stress testing at multiple conditions is applied at the product level of several scaled memory products to assess the performance degradation and product reliability. Acceleration models are derived for each case. For several scaled SDRAM products, retention time degradation is studied and two distinct soft error populations are observed with each technology generation: early breakdown, characterized by randomly distributed weak bits with Weibull slope (beta)=1, and a main population breakdown with an increasing failure rate. Retention time soft error rates are calculated and a multiple failure mechanism acceleration model with parameters is derived for each technology. Defect densities are calculated and reflect a decreasing trend in the percentage of random defective bits for each successive product generation. A normalized soft error failure rate of the memory data retention time in FIT/Gb and FIT/cm2 for several scaled SDRAM generations is presented revealing a power relationship. General models describing the soft error rates across scaled product generations are presented. The analysis methodology may be applied to other scaled microelectronic products and their key parameters.

Microelectronics Reliability↗
NASA NTRS1996

Coarse pointing mechanism assembly for satellite interlink experiment

Since 1975, MECANEX S.A. has been manufacturing components for solar array drives and mechanisms used in space applications. In 1991, work was started in an early phase C (Engineering Model) on a Coarse Pointing Mechanism Assembly (CPMA) for the Semiconductor-laser Inter-satellite Link EXperiment (SILEX). This paper deals with the history, the evolution, and the lessons learned from taking over a pre-design in 1991 to the delivery of last flight models (FM 5 & 6) in 1995.

Maeusli, P. A.↗
DOE OSTI2023

Unveiling the mechanism of remote epitaxy of crystalline semiconductors on 2D materials-coated substrates

Abstract Remote epitaxy has opened novel opportunities for advanced manufacturing and heterogeneous integration of two-dimensional (2D) materials and conventional (3D) materials. The lattice transparency as the fundamental principle of remote epitaxy has been studied and challenged by recent observations defying the concept. Understanding remote epitaxy requires an integrated approach of theoretical modeling and experimental validation at multi-scales because the phenomenon includes remote interactions of atoms across an atomically thin material and a few van der Waals gaps. The roles of atomically thin 2D material for the nucleation and growth of a 3D material have not been integrated into a framework of remote epitaxy research. Here, we summarize studies of remote epitaxy mechanisms with a comparison to other epitaxy techniques. In the end, we suggest the crucial topics of remote epitaxy research for basic science and applications. Graphical Abstract

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Accelerated Scaling to Rapid Open-Air Fabrication of Durable Perovskite Solar Modules

The goals of this SETO project (DE-EE0008559, Accelerated Scaling to Rapid Open-Air Fabrication of Durable Perovskite Solar modules) are to address the principal challenges towards the successful commercialization of perovskite solar modules utilizing scalable, high-throughput open-air spray deposition. The successful outcome of the project will provide the foundation of an all open-air spray deposited perovskite solar module with hole transport layer (HTL), perovskite, electron transport layer (ETL), and barrier layer development while establishing a fundamental understanding of perovskite device behavior under accelerated aging conditions. Through this program, we have made significant progress towards a commercializable pathway for perovskites. (1) We’ve demonstrated the successful open-air deposition of perovskite and transport layer materials. These deposition methods were chosen for their inherent scalability, and open-air processing enables a significant reduction in processing costs. These methods are also compatible with high throughputs, demonstrating the fastest perovskite film deposition at these performance levels. (2) Development of a unique all-fiber laser scribing procedure provides a high-performance, low-cost method for further improvements in scalability. (3) Our group has also placed a unique emphasis on device stability. The development of testing standards for perovskite modules is required to thoroughly evaluate potential candidates for commercialization, and we’ve taken inspiration from current industry standards to provide an honest insight into the performance and reliability of our devices. (4) An extensive cost model detailing the specific contributions of each device layer and production component provides the most thorough evaluation of any perovskite technology against conventional silicon and compound semiconductor solar devices. The cost model is a critical advancement that will provide the foundation for evaluating the levelized cost of energy (LCOE) of this technology.

14 SOLAR ENERGY↗