National Institute Of Standards And Technology Research Articles

The interdependent networked community resilience modeling environment (IN-CORE)

In 2015, the U.S National Institute of Standards and Technology (NIST) funded the Center of Excellence for Risk-Based Community Resilience Planning (CoE), a fourteen university-based consortium of almost 100 collaborators, including faculty, students, post-doctoral scholars, and NIST researchers. This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE - the Interdisciplinary Networked Community Resilience Modeling Environment (IN-CORE). IN-CORE enables communities, consultants, and researchers to set up complex interdependent models of an entire community consisting of people, businesses, social institutions, buildings, transportation networks, water networks, and electric power networks and to predict their performance and recovery to hazard scenario events, including uncertainty propagation through the chained models. The modeling environment includes a detailed building inventory, hazard scenario models, building and infrastructure damage (fragility) and recovery functions, social science data-driven household and business models, and computable general equilibrium (CGE) models of local economies. An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population, economics, physical services, and social services. An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas (CSA) that encompass an array of community resilience metrics (CRM) and support community resilience informed decision-making. Each testbed within IN-CORE has been developed by a team of engineers, social scientists, urban planners, and economists. Community models, begin with a community description, i.e., people, businesses, buildings, infrastructure, and progresses to the damage and loss of functions caused by a hazard scenario, i.e., a flood, tornado, hurricane, or earthquake. This process is accomplished through chaining of modular algorithms, as described. The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models, which have been the least studied area of community resilience but arguably one of the most important. Communities can then test the effect of mitigation and/or policies and compare the effects of “what if” scenarios on physical, social, and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.

Convolutional neural network based key generation for security of data through encryption with advanced encryption standard

Machine learning techniques, especially deep learning, are playing an increasingly important role in our lives. Deep learning uses different models to extract information from the data. They have already had a huge impact in areas such as health (i.e., cancer diagnosis), self-driving cars, speech recognition, and data encryption. Recently, deep learning models, including convolutional neural networks (CNN), have been proven to be more effective in the security field. Moreover, the National Institute of Standards and Technology (NIST) recommends the advanced encryption standard (AES) algorithm as the most often utilized encryption method in several security applications. In this paper, a crypt-intelligent system (CIS) capable of securing data is proposed. It is based on the combination of the performance of CNN with the AES, by substituting the key expansion unit of AES with a CNN architecture that performs the key generation. Our CIS is described using very high-speed integrated circuit (VHSIC) hardware description language (VHDL), simulated by ModelSim, synthesized, and implemented with Xilinx ISE 14.7. Finally, the Airtex-7 series XC7A100T device has achieved an encryption throughput of 965.88 Mbps. In addition, the CIS offers a high degree of flexibility and is supported by reconfigurability, based on the experimental results, if sufficient resources are available, the architecture can provide performance that can satisfy cryptographic applications.

Analysis of Brute Force Attacks Using National Institute Of Standards And Technology (NIST) Methods on Routers

Abstract
 The rapid development of technology, including the internet, can trigger data security issues that harm individuals, organizations, or government agencies. One type of attack often used is a Brute Force attack, which falls under the category of cybercrime. The National Institute of Standards and Technology (NIST) often analyzes digital evidence in these cases. This study analyzes Brute Force attacks using NIST methods on a router that is an additional router to the main router. On the network at the Faculty of Engineering, Universitas Muhammadiyah Magelang (UNIMMA), information about the attack and patterns used by the attacker were successfully obtained, including the IP address and time of the attack. It is concluded that the additional router is vulnerable to Brute Force attacks, and firewall settings are necessary to secure it.

Estimation of measurement uncertainty for the quantification of protein by ID-LC–MS/MS

The emergence of mass spectrometry (MS)-based methods to quantify proteins for clinical applications has led to the need for accurate and consistent measurements. To meet the clinical needs of MS-based protein results, it is important that the results are traceable to higher-order standards and methods and have defined uncertainty values. Therefore, we outline a comprehensive approach for the estimation of measurement uncertainty of a MS-based procedure for the quantification of a protein biomarker. Using a bottom-up approach, which is the model outlined in the “Guide to the Expression of Uncertainty of Measurement” (GUM), we evaluated the uncertainty components of a MS-based measurement procedure for a protein biomarker in a complex matrix. The cause-and-effect diagram of the procedure is used to identify each uncertainty component, and statistical equations are derived to determine the overall combined uncertainty. Evaluation of the uncertainty components not only enables the calculation of the measurement uncertainty but can also be used to determine if the procedure needs improvement. To demonstrate the use of the bottom-up approach, the overall combined uncertainty is estimated for the National Institute of Standards and Technology (NIST) candidate reference measurement procedure for albumin in human urine. The results of the uncertainty approach are applied to the determination of uncertainty for the certified value for albumin in candidate NIST Standard Reference Material® (SRM) 3666. This study provides a framework for measurement uncertainty estimation of a MS-based protein procedure by identifying the uncertainty components of the procedure to derive the overall combined uncertainty.Graphical

Performance Evaluation of AES, ECC and Logistic Chaotic Map Algorithms in Image Encryption

I
 n these days of technology, the usage of images has become increasingly high especially now that almost everyone has access to internet. Also, image helps us to learn, grabs our attention, explains complicated concepts as well as inspires us. Sharing these images is essential and therefore image encryption algorithms are proposed to secure the transmission of these image from many types of attacks such as Man-in-the-middle attack (MITM). In this paper, we proposed a hybrid security system that consist of Elliptic Curve Cryptography (ECC) and Advanced Encryption System (AES). Where ECC is used to generate private/public keys and AES is for encryption and decryption of the image using the ECC generated keys. The system works as follows; it begins by inserting the image to be encrypted which is “lena.png” of size 256*256 and passing it to AES Algorithm along with the generated public key. Then, in AES algorithm, the public key is hashed then used to encrypt the image with AES encryption algorithm. On the other hand, the decryption algorithm works as follows; inserting the encrypted image then calculating the decryption key to use it to decrypt the image using AES decryption algorithm. Finally, our experimental results shows that the National Institute of Standards and Technology (NIST) test shows that the ECC generated keys have better randomness than using only AES generated keys. Also, the encrypted image histogram show that the image pixels values are well distributed across all three channels R, G and B. This shows that the hybrid system is a step further to get a more secure image encryption system against attacks with the generated ECC keys. To get further, Logistic chaotic map has been used to encrypt images for comparison purposes with AES and ECC generated images in terms of randomness, security and histogram

Residual vulnerabilities to power side channel attacks of lightweight ciphers cryptography competition finalists

AbstractThe protection of communications between Internet of Things (IoT) devices is of great concern because the information exchanged contains vital sensitive data. Malicious agents seek to exploit those data to extract secret information about the owners or the system. Power side channel attacks are of great concern on these devices because their power consumption unintentionally leaks information correlatable to the device's secret data. Several studies have demonstrated the effectiveness of authenticated encryption with advanced data, in protecting communications with these devices. A comprehensive evaluation of the seven (out of 10) algorithm finalists of the National Institute of Standards and Technology (NIST) IoT lightweight cipher competition that do not integrate built‐in countermeasures is proposed. The study shows that, nonetheless, they still present some residual vulnerabilities to power side channel attacks (SCA). For five ciphers, an attack methodology as well as the leakage function needed to perform correlation power analysis (CPA) is proposed. The authors assert that Ascon, Sparkle, and PHOTON‐Beetle security vulnerability can generally be assessed with the security assumptions “Chosen ciphertext attack and leakage in encryption only, with nonce‐misuse resilience adversary (CCAmL1)” and “Chosen ciphertext attack and leakage in encryption only with nonce‐respecting adversary (CCAL1)”, respectively. However, the security vulnerability of GIFT‐COFB, Grain, Romulus, and TinyJambu can be evaluated more straightforwardly with publicly available leakage models and solvers. They can also be assessed simply by increasing the number of traces collected to launch the attack.

Attack Resilient True Random Number Generators Using Ferroelectric-Enhanced Stochasticity in 2D Transistor.

By harnessing the physically unclonable properties, true random number generators (TRNGs) offer significant promises to alleviate security concerns by generating random bitstreams that are cryptographically secured. However, fundamental challenges remain as conventional hardware often requires complex circuitry design, showing a predictable pattern that is susceptible to machine learning attacks. Here, a low-power self-corrected TRNG is presented by exploiting the stochastic ferroelectric switching and charge trapping in molybdenum disulfide (MoS2 ) ferroelectric field-effect transistors (Fe-FET) based on hafnium oxide complex. The proposed TRNG exhibits enhanced stochastic variability with near-ideal entropy of ≈1.0, Hamming distance of ≈50%, independent autocorrelation function, and reliable endurance cycle against temperature variations. Furthermore, its unpredictable feature is systematically examined by machine learning attacks, namely the predictive regression model and the long-short-term-memory (LSTM) approach, where nondeterministic predictions can be concluded. Moreover, the generated cryptographic keys from the circuitry successfully pass the National Institute of Standards and Technology (NIST) 800-20 statistical test suite. The potential of integrating ferroelectric and 2D materials is highlighted for advanced data encryption, offering a novel alternative to generate truly random numbers.

US Population Data for 94 Identity-Informative SNP Loci.

The US National Institute of Standards and Technology (NIST) analyzed a set of 1036 samples representing four major US population groups (African American, Asian American, Caucasian, and Hispanic) with 94 single nucleotide polymorphisms (SNPs) used for individual identification (iiSNPs). The compact size of iiSNP amplicons compared to short tandem repeat (STR) markers increases the likelihood of successful amplification with degraded DNA samples. Allele frequencies and relevant forensic statistics were calculated for each population group as well as the aggregate population sample. Examination of sequence data in the regions flanking the targeted SNPs identified additional variants, which can be combined with the target SNPs to form microhaplotypes (multiple phased SNPs within a short-read sequence). Comparison of iiSNP performance with and without flanking SNP variation identified four amplicons containing microhaplotypes with observed heterozygosity increases of greater than 15% over the targeted SNP alone. For this set of 1036 samples, comparison of average match probabilities from iiSNPs with the 20 CODIS core STR markers yielded an estimate of 1.7 × 10-38 for iiSNPs (assuming independence between all 94 SNPs), which was four orders of magnitude lower (more discriminating) than STRs where internal sequence variation was considered, and 10 orders of magnitude lower than STRs using established capillary electrophoresis length-based genotypes.

Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards

The development of quantum computing systems poses a great threat to the security of existing public key-based systems. As a result, the National Institute of Standards and Technology (NIST) started a Post-Quantum Cryptography (PQC) standardization project in 2015, and currently active research is being conducted to apply PQC to various cryptographic protocols. Unlike elliptic curve cryptography (ECC)-based schemes, PQC requires a large memory footprint and key/signature size. Therefore, when migrating PQC to a protocol, depending on the PQC and protocol specifications, it can be hard to migrate PQC. In the case of the WAVE protocol, it is difficult to satisfy the accuracy of a specific PQC algorithm because segmentation of the signature occurs during transmission due to the limitation of the maximum packet size. Therefore, in this paper, we present two methodologies that can apply PQC while complying with IEEE 1609.2 standards to the WAVE protocol in the V2V environment. Whereas previous migration studies have focused on designing a hybrid mode of protocols, this paper explores solutions more intuitively at the application layer of protocols. We analyzed two postquantum digital signature algorithms (Crystals-Dilithium and Falcon) and the structure of basic-safety messages (BSMs) of the V2V protocol on the size side. Through this, we propose methods that can perform an independent signature verification process without waiting for all divided signatures in the WAVE protocol. Our methodology overcomes the limitation that schemes with large signature sizes cannot be mounted into the WAVE protocol. We also note that the architecture used as an on-board unit (OBU) in an autonomous driving environment is mainly a microprocessor. We investigated an optimized PQC implementation in the OBU environment and simulated our methodology with the V2Verifier. Finally, we measured the accurate latency through simulation in Jetson Xavier, which is mainly used as an OBU in the V2V communication network.

Direct Energy Deposition Parametric Simulation Investigation in Gear Repair Applications.

Additive manufacturing technologies have numerous advantages over conventional technologies; nevertheless, their production process can lead to high residual stresses and distortions in the produced parts. The use of numerical simulation models is presented as a solution to predict the deformations and residual stresses resulting from the printing process. This study aimed to predict the tensions and distortions imposed in the gear repair process by directed energy deposition (DED). First, the case study proposed by National Institute of Standards and Technology (NIST) was analyzed to validate the model and the numerically obtained results. Subsequently, a parametric study of the influence of some of the parameters of DED technology was carried out. The results obtained for the validation of the NIST benchmark bridge model were in agreement with the results obtained experimentally. In turn, the results obtained from the parametric study were almost always in line with what is theoretically expected; however, some results were not very clear and consistent. The results obtained help to clarify the influence of certain printing parameters. The proposed model allowed accounting for the effect of residual stresses in calculating the stresses resulting from gear loading, which are essential data for fatigue analysis. Modeling and simulating a deposition process can be challenging due to several factors, including calibrating the model, managing the computational cost, accounting for boundary conditions, and accurately representing material properties. This paper aimed to carefully address these parameters in two case studies, towards reliable simulations.

Review and Comparison of US, EU, and UK Regulations on Cyber Risk/Security of the Current Blockchain Technologies: Viewpoint from 2023

AbstractThe first cryptocurrency was invented in 2008/09, but the Blockchain-Web3 concept is still in its infancy, and the cyber risk is constantly changing. Cybersecurity should also be adapting to these changes to ensure security of personal data and continuation of operations. This article starts with a comparison of existing cybersecurity standards and regulations from the National Institute of Standards and Technology (NIST) and the International Organisation for Standardisation (ISO)—ISO27001, followed by a discussion on more specific and recent standards and regulations, such as the Markets in Crypto-Assets Regulation (MiCA), Committee on Payments and Market Infrastructures and the International Organisation of Securities Commissions (CPMI-IOSCO), and more general cryptography (and post-quantum cryptography), in the context of cybersecurity. These topics are followed up by a review of recent technical reports on cyber risk/security and a discussion on cloud security questions. Comparison of Blockchain cyber risk is also performed on the recent EU standards on cyber security, including European Cybersecurity Certification Scheme (EUCS)—cloud, and US standards—The National Vulnerability Database (NVD) Common Vulnerability Scoring System (CVSS). The study includes a review of Blockchain endpoint security, and new technologies e.g., IoT. The research methodology applied is a review and case study analysing secondary data on cybersecurity. The research significance is the integration of knowledge from the United States (US), the European Union (EU), the United Kingdom (UK), and international standards and frameworks on cybersecurity that can be alighted to new Blockchain projects. The results show that cybersecurity standards are not designed in close cooperation between the two major western blocks: US and EU. In addition, while the US is still leading in this area, the security standards for cryptocurrencies, internet-of-things, and blockchain technologies have not evolved as fast as the technologies have. The key finding from this study is that although the crypto-market has grown into a multi-trillion industry, the crypto-market has also lost over 70% since its peak, causing significant financial loss for individuals and cooperation’s. Despite this significant impact to individuals and society, cybersecurity standards and financial governance regulations are still in their infancy, specifically in the UK.

Area–Time Efficient Implementation of NIST Lightweight Hash Functions Targeting IoT Applications

To mitigate cybersecurity breaches, secure communication is crucial for the Internet of Things (IoT) environment. Data integrity is one of the most significant characteristics of security, which can be achieved by employing cryptographic hash functions. In view of the demand from IoT applications, the National Institute of Standards and Technology (NIST) initiated a standardization process for lightweight hash functions. This work presents field-programmable gate array (FPGA) implementations and carefully worked out optimizations of four Round-3 finalists in the NIST standardization process. A novel compact PHOTON-Beetle implementation is proposed wherein the underlying matrix multiplication is executed in serialized fashion to achieve a small hardware footprint. Sparkle implementations are carried out by implementing the ARX-box in serialized, parallelized, and hybrid approaches. For Ascon and Xoodyak, the proposed implementations compute certain permutation rounds in one clock cycle in order to explore the trade-off between computation time and hardware area. As a result, this work achieves the smallest hardware footprint for PHOTON-Beetle consuming an area 3.4× smaller than state-of-the-art implementations. Ascon and Xoodyak are implemented in a flexible manner that achieves throughput-to-area (TP/A) ratios 1.8× and 3.9× higher, respectively, compared to implementations found in the literature. In addition, we propose the first FPGA implementations for the Sparkle hash function. These efficient implementations provide guidelines for choosing a suitable architecture for applications in demand that can be employed in the IoT environment to achieve data integrity for various applications.

Empirical Validation of Automated Vulnerability Curation and Characterization

Prior research has shown that public vulnerability systems such as US National Vulnerability Database (NVD) rely on a manual, time-consuming, and error-prone process which has led to inconsistencies and delays in releasing final vulnerability results. This work provides an approach to curate vulnerability reports in real-time and map textual vulnerability reports to machine readable structured vulnerability attribute data. Designed to support the time consuming human analysis done by vulnerability databases, the system leverages the Common Vulnerabilities and Exposures (CVE) list of vulnerabilities and the vulnerability attributes described by the National Institute of Standards and Technology (NIST) Vulnerability Description Ontology (VDO) framework. Our work uses Natural Language Processing (NLP), Machine Learning (ML) and novel Information Theoretical (IT) methods to provide automated techniques for near real-time publishing, and characterization of vulnerabilities using 28 attributes in 5 domains. Experiment results indicate that vulnerabilities can be evaluated up to 95 hours earlier than using manual methods, they can be characterized with F-Measure values over 0.9, and the proposed automated approach could save up to 47% of the time spent for CVE characterization.

Reference isotherms for water vapor sorption on nanoporous carbon: results of an interlaboratory study

This paper reports the results of an international interlaboratory study sponsored by the Versailles Project on Advanced Materials and Standards (VAMAS) and led by the National Institute of Standards and Technology (NIST) on the measurement of water vapor sorption isotherms at 25 °C on a pelletized nanoporous carbon (BAM-P109, a certified reference material). Thirteen laboratories participated in the study and contributed nine pure water vapor isotherms and four relative humidity isotherms, using nitrogen as the carrier gas. From these data, reference isotherms, along with the 95% uncertainty interval (Uk=2), were determined and are reported in a tabular format.

Rapid Analysis of Drugs: A Pilot Surveillance System To Detect Changes in the Illicit Drug Supply To Guide Timely Harm Reduction Responses - Eight Syringe Services Programs, Maryland, November 2021-August 2022.

A record number of 2,912 drug overdose deaths occurred in Maryland during the 12-month period July 1, 2020-June 30, 2021. Illicitly manufactured fentanyl, fentanyl analogs, or both* were involved in 84% of these deaths.† Timely identification of illicit drug market changes (e.g., fentanyl rapidly replacing heroin) could improve the public health response, specifically communications about risks for novel psychoactive substances. During November 19, 2021-August 31, 2022, the National Institute of Standards and Technology (NIST)§ tested 496 deidentified drug paraphernalia samples that staff members collected at eight Maryland syringe services programs (SSPs), also known as needle exchange programs,¶ in partnership with the Maryland Department of Health Center for Harm Reduction Services (CHRS).** All test results were available within 48 hours. Among the 496 paraphernalia samples collected, 367 (74.0%) tested positive for an opioid, and 364 (99.2%) of these samples contained fentanyl or fentanyl analogs. Approximately four fifths of fentanyl-positive samples also tested positive for the veterinary medicine xylazine, a sedative that when combined with opioids might increase the potential for fatal respiratory depression and soft tissue infections when injected (1). For 248 of the 496 samples, SSP participants also completed a questionnaire about the drugs they had intended to purchase. Among the 212 participants who had intended to buy an opioid, 87.7% were exposed to fentanyl, fentanyl analogs, or both, and 85.8% were unknowingly exposed to xylazine. Results improved awareness of fentanyl and xylazine among SSP staff members and galvanized efforts to enhance SSPs' wound care services for participants experiencing soft tissue injuries possibly associated with injecting xylazine. Rapid analysis of drug paraphernalia can provide timely data on changing illicit drug markets that can be used to mitigate the harms of drug use more effectively.

Extractive-liquid sampling electron ionization-mass spectrometry (E-LEI-MS): a new powerful combination for direct analysis

One of modern analytical chemistry main challenges is providing as fast as possible results in different application fields. In this view, real-time analysis techniques are experiencing ever-increasing success as they can provide data quickly, almost without sample preparation steps. Most of real-time approaches are based on direct mass spectrometry (DMS), a method of analyzing samples without the need for separation or pre-treatment steps. Instead, the sample is directly introduced into the mass spectrometer for analysis. In this context, ambient ionization mass spectrometry (AIMS) techniques are widely represented and successfully used. Extractive-liquid sampling electron ionization-mass spectrometry (E-LEI-MS) represents a different analytical strategy that allows coupling ambient sampling with electron ionization (EI), avoiding any sample preparation step and providing identification based on the comparison with the National Institute of Standards and Technology (NIST) library spectra. E-LEI-MS consists of a dispositive for solvent release and sampling at ambient conditions coupled with an EI source of a single quadrupole mass spectrometer. A micromanipulator allows fine (x,y,z) positioning of a sampling tip. MS can operate in scan or SIM modes depending on the application goals and requirements. Several preliminary successful results were already obtained due to the highly informative EI mass spectra generation. The system was applied to the analysis of active ingredients in pharmaceutical tablets, pesticides on fruit peel, a drug of abuse (cocaine) determination in banknotes, and analysis of unknown components on painting surfaces. Both forensic and artwork applications allowed determining the spatial distribution of the analytes. Here we present a proof-of-concept of E-LEI-MS for targeted/non-targeted analysis and semi-quantitative detection.

Deep learning health management diagnostics applied to the NIST smoke experiments

Fire is one of the most important hazards that must be considered in advanced nuclear power plant safety assessments. The Nuclear Regulatory Commission (NRC) has developed a large collection of experimental data and associated analyses related to the study of fire safety. In fact, computational fire models are based on quantitative comparisons to those experimental data. During the modeling process, it is important to develop diagnostic health management systems to check the equipment status in fire processes. For example, a fire sensor does not directly provide accurate and complex information that nuclear power plants (NPPs) require. With the assistance of the machine learning method, NPP operators can directly get information on local, ignition, fire material of an NPP fire, instead of temperature, smoke obscuration, gas concentration, and alarm signals. In order to improve the predictive capabilities, this work demonstrates how the deep learning classification method can be used as a diagnostic tool in a specific set of fire experiments. Through a single input from a sensor, the deep learning tool can predict the location and type of fire. This tool also has the capability to provide automatic signals to potential passive fire safety systems. In this work, test data are taken from a specific set of the National Institute of Standards and Technology (NIST) fire experiments in a residential home and analyzed by using the machine learning classification models. The networks chosen for comparison and evaluation are the dense neural networks, convolutional neural networks, long short-term memory networks, and decision trees. The dense neural network and long short-term memory network produce similar levels of accuracy, but the convolutional neural network produces the highest accuracy.

Threat modeling in smart firefighting systems: Aligning MITRE ATT&CK matrix and NIST security controls

Industrial automation technologies are envisioned as multi-device systems that are constantly interacting with one another and with enterprise systems. In these industrial systems, the industrial internet of things (IIoT) significantly improves system efficiency, scalability, ease of control, and monitoring. These benefits have been achieved at the cost of greater security risks, thus making the system vulnerable to cyberattacks. Historically, industrial networks and systems lacked security features like authentication and encryption due to intended isolation over the Internet. Lately, remote access to these IIoT systems has made an attempt of holistic security alarmingly critical. In this research paper, a threat modeling framework for smart cyber–physical system (CPS) is proposed to get insight of the potential security risks. To carry out this research, the smart firefighting use case based on the MITRE ATT&CK matrix was investigated. The matrix analysis provided structure for attacks detection and mitigation, while system requirement collection (SRC) was applied to gather generic assets’ information related to hardware, software and network. With the help of SRC and MITRE ATT&CK, a threat list for the smart firefighting system was generated. Conclusively, the generated threat list was mapped on the national institute of standards and technology (NIST) security and privacy controls. The results show that these mapped controls can be well-utilized for protection and mitigation of threats in smart firefighting system. In future, critical cyber–physical systems can be modeled upon use case specific threats and can be secured by utilizing the presented framework.

Review of the key milestones in the development of critical dimension small angle x-ray scattering at National Institute of Standards and Technology

An x-ray scattering based metrology was conceived over 20 years ago as part of a collaboration between National Institute of Standards and Technology (NIST) and International Business Machines Corporation (IBM) to evaluate the performance of lithographic patterning materials for the semiconductor industry. This methodology treated a periodic array of lithographic structures as a diffraction grating and focused on extracting the physical dimensions of the structures in the grating by analyzing the diffraction patterns. In the early stages of the work the focus was on developing the transmission small-angle x-ray scattering (tSAXS) as a metrology tool to measure the critical dimensions (CD) of the lithographic features vital to the integrated circuit chip fabrication. Later, the focus shifted to include grazing incident small-angle x-ray scattering and x-ray reflectivity as parts of the CD metrology tool due to their unique capabilities. Frequently the term critical dimension small-angle x-ray scattering (CDSAXS) has been used as a synonym for the metrology of using tSAXS for CD measurements without mentioning transmission. Various milestones in the CDSAXS development are reviewed in this article together with some prospects regarding the future growth of x-ray-based metrology for complex three-dimensional nanostructures important to semiconductor industries.

Preface

Proceedings of the joint 13th and 14th International Conferences on Polarized Neutrons for Condensed-Matter Investigations The international Conference on Polarized Neutrons for Condensed-Matter Investigations (PNCMI) started its long journey in Dubna, Russia in 1996 and has taken place biennially since then. The present volume of proceedings represents the 13th and 14th editions of this series. The 13th PNCMI was originally suggested by the International Advisory Committee (IAC) of PNCMI to be held in Maryland, USA in 2020, and was postponed due to the fast-spreading COVID-19 pandemic. The IAC later suggested, due to the still evolving pandemic, to replace PNCMI 2020 with a virtual PNCMI in 2021 and an in-person PNCMI in 2022.The 13th PNCMI, organized by National Institute of Standards and Technology (NIST), was held online from July 27-30, 2021. One of the main objectives was to keep up the frequency of PNCMI conferences for the community, staying up to date on emerging topics using polarized neutrons and on related instrumentation development. 110 participants from 40 institutions and 15 countries crossing Europe, Asia, Australia, and North America attended the virtual conference. The conference was limited to invited speaker and poster sessions since it was run two- and one-half hours daily to accommodate different time zones for the international attendees.The 14th PNCMI was held in-person from July 25-29, 2022 in Annapolis, Maryland, USA and jointly organized by the University of Maryland and NIST. The number of conference participants was affected by international travel restrictions due to the pandemic. The conference attendees included 62 participants from 30 institutions and 10 countries crossing Europe, Asia, and North America. The scientific program featured a Keynote lunch presentation, titled “Polarization can be good”, given by Dr. Charles Majkrzak (NCNR), invited, and contributed oral talks, and poster presentations.The PNCMI conference covers the latest condensed-matter investigations using polarized neutrons and state-of-the-art methodologies and techniques of polarized-neutron production and utilization for novel instrumentation and experiments. The 13th and 14th editions covered the following traditional topics: multiferroics and chirality, strongly correlated electron systems, frustrated and disordered systems, magnetic nanomaterials, thin films and multilayers, imaging, polarized neutron instrumentation and polarized neutron techniques and methods. Quantum materials as an emerging topic was included in the 13th PNCMI as well. A new session, -- polarized quasi-elastic neutron scattering (QENS) driven by recently growing interest and benefitting from modernized polarized neutron spectroscopies, was also included for the first time in the 14th PNCMI.A two-day Polarized Neutron School, for 12 graduate students and young postdoctoral researchers familiar with scattering techniques, was held at the NIST Center for Neutron Research (NCNR) preceding the 2022 in-person conference.We would like to take this opportunity again to thank all participants for their attendance, and the International Advisory Committee for guidance in developing an excellent scientific program. We’d like to extend a special thank you to the University of Maryland and the NIST conference services for all their hard work and planning to help organize and ensure it ran smoothly. The 14th PNCMI was made possible with the generous support of Cryogenic Limited, HTS-110, Mirrotron, ORNL, and SwissNeutronics.List of International Advisory Committee, Local Organizing Committee are available in this Pdf.