Process Variations Research Articles

A novel FVF-based GHz-range biquad in a 28 nm CMOS FD-SOI technology

Inductor-less CMOS filters with bandwidth exceeding several GHz are required in high-speed data converter applications. This paper introduces two complementary biquad filters, one N-based and the other P-based, utilizing the well-established flipped voltage follower (FVF) stage. These filters exhibit more than 7 GHz cut-off frequency and a low power consumption of 0.54 mW/pole for the N-type biquad, and 0.3 mW/pole for the P-type one, demonstrating impressive figures-of-merit (FOMs) even considering bandwidth and dynamic range. The implementation of these biquads in the STMicroelectronics FD-SOI 28-nm CMOS process, along with extensive simulations, ensures stable performance under process, supply voltage and temperature (PVT) variations and mismatches, as confirmed by post-layout simulations. Notably, the area occupied by each biquad is merely 246 μm2 for N-type biquad and 193 μm2 for P-type, marking one of the smallest footprints in the existing literature. The achieved figures-of-merit are noteworthy, showcasing excellent power efficiency, minimal area occupation, and commendable dynamic range.

ROM-based stochastic optimization for a continuous manufacturing process

This paper proposes a model-based optimization method for the production of automotive seals in an extrusion process. The high production throughput, coupled with quality constraints and the inherent uncertainty of the process, encourages the search for operating conditions that minimize nonconformities. The main uncertainties arise from the process variability and from the raw material itself. The proposed method, which is based on Bayesian optimization, takes these factors into account and obtains a robust set of process parameters. Due to the high computational cost and complexity of performing detailed simulations, a reduced order model is used to address the optimization. The proposal has been evaluated in a virtual environment, where it has been verified that it is able to minimize the impact of process uncertainties. In particular, it would significantly improve the quality of the product without incurring additional costs, achieving a 50% tighter dimensional tolerance compared to a solution obtained by a deterministic optimization algorithm.

Low Power CMOS Differential Amplifiers through ACM Model: Process Variations and Yield Prediction

Low Power CMOS Differential Amplifiers through ACM Model: Process Variations and Yield Prediction

Blood far forward: A cross-sectional analysis of prehospital transfusion practices in the Canadian Armed Forces

BackgroundCanadian Armed Forces (CAF) operate in environments that challenge patient care, especially trauma. Military personnel often find themselves in remote settings without conventional healthcare facilities. Treating traumatic injuries, particularly hemorrhagic shock, often necessitates prehospital blood transfusion. This study aims to present an overview of the current CAF prehospital transfusion practices. Furthermore, the study compared current and developing protocols against expert-recommended guidelines. MethodsA cross-sectional survey design was employed to describe and compare CAF prehospital blood transfusion practices and protocols against expert recommendations. Topics included protocols, equipment, and procedures. An online survey targeted medical leadership and providers within CAF, with data collected from August 15 to December 15, 2023. Results were summarized descriptively. This study received approval from the Unity Health Toronto Research Ethics Board (REB 23–087). ResultsUnits and teams with prehospital blood transfusion capabilities were contacted, achieving a 100 % response rate. Within CAF, Canadian Special Operations Forces Command (CANSOFCOM), Mobile Surgical Resuscitation Team (MSRT), and Canadian Medical Emergency Response Team (CMERT) possess these capabilities, established between 2013 and 2018. These programs are crucial for military operations. CAF has access to standard blood components, cold Leuko-Reduced Whole Blood (LrWB), and factor concentrates from Canadian Blood Services (CBS), available for both domestic and international missions given adequate planning and favorable conditions. Key findings indicate high adherence to recommended practices, some variability in the transfusion process, and potential benefits of standardizing prehospital transfusion practices. ConclusionsThis study provided insights into CAF's implementation of prehospital transfusion practices, highlighting high adherence to national expert recommendations and the importance of structured protocols in military prehospital trauma management. Implications of key findingsCAF's approach and adoption of prehospital transfusion protocols lay a strong foundation for managing trauma patients in remote settings and for expanding prehospital transfusion capabilities across CFHS deployed assets. Further research is needed to advance military trauma care by adapting prehospital blood transfusion to dynamic tactical landscapes and evolving technologies.

A deep learning-based convolutional spatiotemporal network proxy model for reservoir production prediction

Accurate production prediction is crucial in the field of reservoir management and production optimization. Traditional models often struggle with the complexities of nonlinear relationships and high-dimensional data, which hinders their ability to capture the variability of the production process efficiently and results in time-consuming calculations. To overcome these limitations, this paper introduces an innovative proxy modeling technique employing a convolutional spatiotemporal neural network. This method utilizes convolutional neural networks to extract spatial features from high-dimensional data, while the Transformer is used to model and predict complex temporal dynamics in production. To validate the effectiveness of the proposed proxy model, two case studies involving four injection and nine production wells within two-dimensional (2D) and three-dimensional (3D) non-homogeneous reservoirs were conducted, with the R2 coefficient serving as the primary evaluation metric. As the number of training iterations and data volume increase, the proxy model demonstrates rapid convergence. In tests conducted on the 2D and 3D datasets, the average R2 value exceeded 0.96 and 0.94. These results confirm the accuracy and stability of the proxy model. It also shows that the proxy model can accurately describe the geological and fluid seepage characteristics of the reservoir, which in turn can achieve a highly accurate match with the real data. In addition, the computational time is reduced by two orders of magnitude compared to traditional models. Compared with the long short-term memory method, the accuracy of the prediction results is increased by 30%, which greatly enhances efficiency and accuracy. To some extent, the presented proxy model can provide some guidance for the efficient history match of production data.

A fuzzy control algorithm based on artificial intelligence for the fusion of traditional Chinese painting and AI painting

Recently, artificial intelligence (AI)-generated resources have gained popularity because of their high effectiveness and reliability in terms of output and capacity to be customized and broadened, especially in image generation. Traditional Chinese paintings (TCPs) are incomplete because their color contrast is insufficient, and object reality is minimal. However, combining AI painting (AIP) with TCP remains inadequate and uncertain because of image features such as patterns, styles, and color. Hence, an algorithm named variational fusion-based fuzzy accelerated painting (VF2AP) has been proposed to resolve this challenge. Initially, the collected TCP data source is applied for preprocessing to convert it into a grayscale image. Then, the feature extraction process is performed via fuzzy-based local binary pattern (FLBP) and brushstroke patterns to enhance the fusion of intelligent fuzzy logic to optimize the local patterns of textures in a noisy image. Second, the extracted features are used as inputs to the variational autoencoder (VAE), which is used to avoid latent space irregularities in the image and the reconstructed image by maintaining minimum reconstruction loss. Third, fuzzy inference rules are applied to avoid variation in the fusion process of the reconstructed and original images. Fourth, the feedback mechanism is designed with evaluation metrics such as area under the curve-receiver operating characteristic (AUC-ROC) analysis, mean square error (MSE), structural similarity index (SSIM), and Kullback‒Leibler (KL) divergence to enhance the viewer's understanding of fused painting images.

Radiation Damage on Silicon Photomultipliers from Ionizing and Non-Ionizing Radiation of Low-Earth Orbit Operations.

Silicon Photomultipliers (SiPMs) are single photon detectors that gained increasing interest in many applications as an alternative to photomultiplier tubes. In the field of space experiments, where volume, weight and power consumption are a major constraint, their advantages like compactness, ruggedness, and their potential to achieve high quantum efficiency from UV to NIR makes them ideal candidates for spaceborne, low photon flux detectors. During space missions however, SiPMs are usually exposed to high levels of radiation, both ionizing and non-ionizing, which can deteriorate the performance of these detectors over time. The goal of this work is to compare process and layout variation of SiPMs in terms of their radiation damage effects to identify the features that helps reduce the deterioration of the performance and develop the next generation of more radiation-tolerant detectors. To do this, we used protons and X-rays to irradiate several Near Ultraviolet High-Density (NUV-HD) SiPMs with small areas (single microcell, 0.2 × 0.2 mm2 and 1 × 1 mm2) produced at Fondazione Bruno Kessler (FBK), Italy. We performed online current-voltage measurements right after each irradiation step, and a complete functional characterization before and after irradiation. We observed that the main contribution to performance degradation in space applications comes from proton damage in the form of an increase in primary dark count rate (DCR) proportional to the proton fluence and a reduction in activation energy. In this context, small active area devices show a lower DCR before and after irradiation, and we propose light or charge-focusing mechanisms as future developments for high-sensitivity radiation-tolerant detectors.

On-chip based power estimation for CMOS VLSI circuits using support vector machine

Power estimation has a major impact on the reliability of very-large-scale integration (VLSI) circuits. As a results power estimation is highly needed in VLSI circuits at the early stages. One of the evident challenges in integrated circuit (IC) industry is development and investigation of techniques for the reduction of design complexity due to the growing process variations and reduction of chip manufacturing turnaround time. Under these conditions, the higher design levels of average power estimation before the chip manufacturing process is highly essential for the calculation of power budget and to take the necessary steps for the reduction of power consumption. Over the years, most of the approaches were designed to estimate the power usage, however, most of the conventional techniques are time consuming, resource-intensive and largely manual. Machine learning techniques have received much attention in many of the engineering applications and are capable for modelling the complex systems through historical data. Hence, in this work on chip-based power estimation for complementary metal-oxide-semiconductor (CMOS) VLSI using support-vector machine (SVM) is presented to estimate the power. The SVM is employed to estimate the usage of power at runtime. The performance of this model is evaluated in terms of Power usage, delay, data accuracy and error rate.

A New Development of Cross-Correlation-Based Flow Estimation Validated and Optimized by CFD Simulation

The accurate measurement of mass flow rates is important in nuclear power plants. Flow meters have been invented and widely applied in several industries; however, the operating environment in advanced nuclear power plants is especially harsh due to high temperatures, high radiation, and potentially corrosive conditions. Traditional flow meters are largely limited to deployment at the outlet of pumps, on pipes, or in limited geometries. Cross-correlation function (CCF) flow estimation, on the other hand, can estimate the flow velocity indirectly without any specific instruments for flow measurement and in any geometry of the flow region. CCF flow estimation relies on redundant instruments, typically temperature sensors, in series in the direction of flow. One challenge for CCF flow estimation is that the accuracy of the flow measurement is mainly determined by inherent, common local process variation across the sensors, which may be small compared to the uncorrelated measurement noise. To differentiate the process variations from the uncorrelated noise, this research implements periodic fluid injection at a different temperature than the bulk fluid before the temperature sensors to amplify process variation. The feasibility and accuracy of this method are investigated through flow loop experiments and Computational Fluid Dynamics (CFD) simulations. This paper focuses on a CFD simulation model to verify the previous experimental results and optimize CCF flow estimation with different configurations. The optimization study is carried out to perform a grid search on the optimal location of the sensor pair under different flow rates. The CFD results show that the optimal sensor spacing depends on the flow rate being measured and provides guidance for sensor location implementation under various anticipated flow rates.

PENGEMBANGAN MODUL AJAR BERDIFERENSIASI MATA PELAJARAN BAHASA INDONESIA KELAS II MATERI KALIMAT TANYA

Students have a diversity of needs that are often not well accommodated by teachers. This study aims to develop and determine the quality of differentiated learning teaching modules for subjects Indonesian question sentence material for grade II students. The development of differentiated teaching modules is expected to assist teachers in compiling learning activities based on the diversity of students. This research was carried out at Mangunan Experimental Elementary School using ADDIE type Research and Development research. The teaching module developed contains the steps for making and examples of learning designs. This research was validated by three validators who then tested the module on a learning group of 26 grade 2 students with the results of the initial assessment having different learning readiness, interests and learning styles, and four students with special needs: autism, slow learners, speech delay, and ADHD. Based on the results of expert validation, the quality of teaching modules is stated to be very good judging from the module criteria, namely: containing essential material, fostering interest, challenging, relevant, providing learning based on the results of assessment of student needs, presenting variations in the learning process, content, products, and environment. The results of the trial at the first meeting, showed an increase in understanding by 73.90% in learning question sentences. Learners show enthusiasm and increased involvement in the learning process as their learning readiness, interests, and learning styles are accommodated. This research is limited to the trial of the first meeting of the teaching module and still requires trials for subsequent meetings for more comprehensive results

Sliding and Adaptive Windows to Improve Change Mining in Process Variability

A configurable process Change Mining approach can detect changes from a collection of event logs and provide details on the unexpected behavior of all process variants of a configurable process. The strength of Change Mining lies in its ability to serve both conformance checking and enhancement purposes; users can simultaneously detect changes and ensure process conformance using a single, integrated framework. In prior research, a configurable process Change Mining algorithm has been introduced. Combined with our proposed preprocessing and change log generation methods, this algorithm forms a complete framework for detecting and recording changes in a collection of event logs. Testing the framework on synthetic data revealed limitations in detecting changes in different types of variable fragments. Consequently, it is recommended that the preprocessing approach be enhanced by applying a filtering algorithm based on sliding and adaptive windows. Our improved approach has been tested on various types of variable fragments to demonstrate its efficacy in enhancing Change Mining performance.

A novel logistic regression-embedded 0–1 mixed-integer probabilistic robust design model to obtain optimum sustainable factor settings

Quality by design (QbD) focuses on satisfying the needs of products or processes when aiming for variance reduction. On the other side, sustainability is associated with carrying on to conduct so over time. The research gap is also addressed in sustainability and QbD in this paper. Next, robust design (RD) is a suitable approach to QbD to reduce the process or product variation regarding quantitative and qualitative design factors. For a number of situations, a binomial random response variable is considered to decrease the variability of the production process. Thus, this paper is four-fold. First of all, a modified distance-based criterion is developed to generate the design matrix when dealing with quantitative and qualitative design factors. Second, a logistic regression technique is presented for modeling responses when the random response variable is binomial. Third, a new logistic regression-based 0–1 mixed-integer probabilistic RD model is proposed to find optimum sustainable design factors. Fourth, an Industry 4.0 associated case study is performed in order to design sustainable process development, and the comparison and verification studies are also presented to show the effectiveness of the proposed probabilistic model. Further, managerial insights are provided based on the sustainable development goals (SDGs).

Part feeding scheduling for mixed-model assembly lines with autonomous mobile robots: benefits of using real-time data

ABSTRACT Mixed-model assembly is increasingly widespread to meet customer requirements for customisation and short delivery times. Flexible part feeding systems are required to timely replenish assembly stations with materials, avoid station idle times, and limit inventory levels on the shop floor. Part feeding scheduling is a complex and dynamic problem, affected by processing time fluctuations, equipment failures, and variations of product mix. Although real-time data of factory processes and resources is widely available and can be exploited using a digital twin of the part feeding system, there is a lack of scientific evidence on the benefits of using real-time data in part feeding scheduling. This research addresses this gap by developing an agent-based simulation model of a part feeding system with a fleet of autonomous mobile robots (AMRs) and comparing a real-time dynamic part feeding scheduling approach with static benchmark approaches. Numerical results indicate that using real-time data improves the performance of the part feeding system and the assembly system significantly, and allows improving the trade-off between the AMR fleet size and the total storage capacity on the shop floor, resulting in lower investment costs for AMRs given a certain storage capacity or lower required storage capacity given an AMR fleet.

Analyzing Aging Effects on SRAM PUFs: Implications for Security and Reliability

The aging effects on Static Random-Access Memory Physical Unclonable Functions (SRAM PUFs) pose significant security and reliability challenges for current hardware systems. SRAM PUFs utilize process variations in integrated circuits for secure key generation and device authentication. However, aging effects such as bias temperature instability (BTI) and hot carrier injection (HCI) may change SRAM cell characteristics and affect PUF responses. This study addresses various aging-induced variation challenges, identifies security vulnerabilities, and provides innovative approaches to risk reduction. Furthermore, this study focuses on concerns about SRAM PUF reliability. In this regard, mitigation approaches like adaptive reconfiguration, error correction codes, and multi-modal PUFs are introduced to enhance the resilience of SRAM PUFs. This study concludes by outlining future research directions and prospects for improving SRAM PUF-based security solutions by several orders of magnitude. This is carried out with various complexities related to semiconductor device aging.

An octave tuning range quad-core VCO using a multi-mode resonator

This paper presents a novel multi-core multi-mode electric-magnetic (E-M) mixed-coupling voltage-controlled oscillator (VCO), achieving wide tuning range and phase noise optimization simultaneously through a multi-core structure. A symmetric multi-mode resonator is proposed to address the issue of differential signal mismatch caused by transformer asymmetry. An inductor ring is used to balance the inductance of each mode and its effect in each mode is analyzed in detail. A symmetrical switch network is employed for mode switching, offering advantages such as reduced parasitics and mismatch. The utilization of a varactor combined with a fixed capacitor enables electrical coupling, ensuring overlapping frequency bands even with variations in process, voltage, and temperature (PVT), and extending the tuning range. Furthermore, a switched resistor array is employed to bias the negative resistance pair and minimize the contribution of 1/f noise upconversion to the phase noise. Designed in a 40-nm complementary metal–oxide–semiconductor (CMOS) technology with a core area of 0.16 mm2, the VCO achieves a simulated continuous tuning range of 81.5%, spanning from 11.1 to 26.4 GHz. Operating at a 1.1 V supply with a power consumption of 16 mW, the phase noise at 12.15 GHz is −118.6 dBc/Hz at a 1 MHz offset, corresponding to a figure-of-merit (FoM) of 188.3 dBc/Hz and a FoMT of −206.3 dBc/Hz.

The Effect of Different Bases of Vegetable and Carrageenan Concentrations on The Quality of The Mix Vegetable Leather of Lettuce and Mustars Greens

The benefits of each vegetable are different, one vegetable cannot be substituted for another. Vegetable processing in Indonesia is still very limited to sautéed, boiled, steamed or eaten directly, meaning that people's interest in vegetable consumption is still relatively low. However, increasing public awareness of the use of vegetables encourages variations in vegetable processing. One of the processing that can be applied to vegetables is manufacturing mix vegetable leather. Vegetable leather is a processed product derived from crushed and dried vegetables. The diverse benefits of each vegetable encourage its creationmix vegetable leather orvegetable leather which uses more than 1 vegetable as the basic ingredient. Mustard greens and lettuce are vegetables with high production in Indonesia. Apart from that, the crude fiber from mustard greens is 51.07%, while lettuce contains 50.7% crude fiber. The purpose of this study is to obtain the effect of vegetable basis and carrageenan concentration variance on mix vegetable leather of lettuce and mustard greens. The increase of people awareness of vegetable utilization drives to variate the vegetable processing. Each vegetable has its own unique benefits thus they could not be easily subtituted and mixed. This study used RAL with two factors, those are variety of vegetable basis between lettuce and mustard greens (30,50,100) and carrageenan consentration (1% and 1,5%). The chemical respond in this study covers moisture content, ash content, and crude fiber, while the organoleptic respond covers color, aroma, flavor, and texture. The best treatment is found on A1B1, that has 30 g for vegetable basis and 1% of carrageenan consentration with 13,31 % moisture content, 12,29% ash content, 8,24% crude fiber, 3,7 score in color and 2,05 color quality; aroma 3.23 and aroma quality 2.80; taste 3.40 and taste quality 3.06; and texture 3.46 and texture quality 3.73.

An outbreak of renal failure in asian dogs due to s-triazine adulteration of pet food raw material: Analysis of unique, green kidney stones formed as a result

Renoliths were removed at necropsy from dogs that had died from acute kidney injury in Asia in 2004 and submitted to our laboratories for analysis including elemental composition, mass spectrometry, and nuclear magnetic resonance spectroscopy. The presence of a mixed s-triazine matrix comprising melamine, cyanuric acid, and ammelide, but no detectable ammeline, was found in the stone samples we analyzed. The unusual and unique green coloration of these stones was determined to be due to the presence of biliverdin. The occurrence of these green stones distinguished the 2004 incident from another incident in 2007 in the USA and other reported cases. The presence of crystals was reported in renal tubules and collecting ducts in both outbreaks, but no stones were reported in the 2007 incident. This difference suggested a variation in the disease process caused by mixed s-triazine ingestion. Careful monitoring of food additives is warranted to prevent future problems in animals and humans.

Smart Industrial Internet of Things Framework for Composites Manufacturing.

Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits.

Machine learning techniques for quality assurance in additive manufacturing processes

Additive manufacturing (AM) processes have revolutionized manufacturing industries by enabling the production of complex geometries with reduced material waste and lead times. However, ensuring the quality of AM parts remains a significant challenge due to the complexity of the process and inherent variability in material properties. This review investigates the use of artificial intelligence (AI) to enhance quality assurance in AM processes, focusing on specific machine learning techniques such as convolutional neural networks for defect detection, support vector machines for classification of material properties, and reinforcement learning for real-time process optimization. The AI-driven methodologies are applied to predict defects, optimize process parameters, and monitor real-time production quality, utilizing large datasets generated from sensors and in-situ monitoring systems. The study demonstrates significant improvements in the accuracy of defect detection, the reliability of material property classification, and the efficiency of process optimization. In addition, it addresses challenges such as data pre-processing, model interpretability, and integration with existing AM systems. The findings highlight the potential of AI to transform quality assurance in AM and outline future research directions for further integration and enhancement of AI techniques in AM.

An Ultra-Low-Voltage Approach to Accurately Set the Quiescent Current of Digital Standard Cells Used for Analog Design and Its Application on an Inverter-Based Operational Transconductance Amplifier

An approach to design analog building blocks based on digital standard cells is presented in this work. By ensuring that every CMOS inverter from a standard-cell library operates with a well-defined quiescent current and output voltage, the suggested method makes it possible to construct analog circuits that are resistant against PVT variations. The method uses the local supply voltages connected to the source terminals of the p-channel and n-channel MOS transistors of the standard-cell inverters as control inputs. It is based on adaptive supply voltage generator (ASVG) reusable blocks, which are comparable to those used in digital applications to handle process variations. All of the standard-cell inverters used for analog functions receive the local supply voltages produced by the ASVGs, which enable setting each cell’s quiescent current to a multiple of a reference current and each cell’s static output voltage to an appropriate reference voltage. Both the complete custom design of the ASVG blocks and a theoretical study of the feedback loop of the ASVG are presented. An application example through the design of a fully synthesizable two-stage operational transconductance amplifier (OTA) is also provided. The TSMC 180 nm CMOS technology has been used to implement both the OTA and the ASV generators. Simulation results have demonstrated that the proposed approach allows to accurately set the quiescent current of standard-cell inverters, dramatically reducing the effect of PVT variations on the pmain performance parameters of the standard-cell-based two-stage OTA.