Process Variations Research Articles

Sum rule comparison of narrowband and broadband sum frequency generation spectra and comparison with theory

Earlier sum frequency generation (SFG) experiments involve one infrared and one visible laser, and a measurement of the intensity of the response, yielding data on the surface sensitive properties of the sample. Recently, both the real and imaginary components of the susceptibility were measured in two different sets of experiments. In one set, a broadband infrared laser was used, permitting observations at very short times, while in another set the infrared laser was narrowband, permitting higher spectral resolution. The differences in the spectrum obtained by the two will be most evident in studying narrow absorption bands, e.g., the band due to dangling OH bonds at a water interface. The direct comparisons in the integrated amplitude (sum rule) of the imaginary part of the dangling OH bond region differ by a factor of 3. Due to variations in experimental setup and data processing, corrections were made for the quartz reference, Fresnel factors, and the incident visible laser wavelength. After the corrections, the agreement differs now by the factors of 1.1 within broadband and narrowband groups and the two groups now differ by a factor of 1.5. The 1.5 factor may arise from the extra heating of the more powerful broadband laser system on the water surface. The convolution from the narrowband SFG spectrum to the broadband SFG spectrum is also investigated and it does not affect the sum rule. Theory and narrowband experiments are compared using the sum rule and agree to a factor of 1.3 with no adjustable parameters.

Probing single electrons across 300-mm spin qubit wafers

Building a fault-tolerant quantum computer will require vast numbers of physical qubits. For qubit technologies based on solid-state electronic devices1–3, integrating millions of qubits in a single processor will require device fabrication to reach a scale comparable to that of the modern complementary metal–oxide–semiconductor (CMOS) industry. Equally important, the scale of cryogenic device testing must keep pace to enable efficient device screening and to improve statistical metrics such as qubit yield and voltage variation. Spin qubits1,4,5 based on electrons in Si have shown impressive control fidelities6–9 but have historically been challenged by yield and process variation10–12. Here we present a testing process using a cryogenic 300-mm wafer prober13 to collect high-volume data on the performance of hundreds of industry-manufactured spin qubit devices at 1.6 K. This testing method provides fast feedback to enable optimization of the CMOS-compatible fabrication process, leading to high yield and low process variation. Using this system, we automate measurements of the operating point of spin qubits and investigate the transitions of single electrons across full wafers. We analyse the random variation in single-electron operating voltages and find that the optimized fabrication process leads to low levels of disorder at the 300-mm scale. Together, these results demonstrate the advances that can be achieved through the application of CMOS-industry techniques to the fabrication and measurement of spin qubit devices.

Experimental Design of the Adhesion between a PEI/Glass Fiber Composite and the AA1100 Aluminum Alloy with Oxide Coating Produced via Plasma Electrolytic Oxidation (PEO)

In this study, the AA1100 aluminum alloy underwent the plasma electrolytic oxidation (PEO) process to enhance its adhesion to a thermoplastic composite of polyetherimide (PEI) reinforced with glass fiber, following ASTM D1002:10 standards. A 23 factorial design was employed, varying three parameters in the oxidation process: immersion time, applied electric potential, and electrolyte concentration (Na2B4O7). The joining of aluminum and thermoplastic composite samples was achieved through oxy-fuel welding (OFW), using oxygen and acetylene gases. For the characterization of the joined samples, a universal tensile testing machine was utilized with a displacement speed of 1.5 mm/min. The analysis of the oxide coating involved scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Fourier transform infrared spectroscopy (FT-IR). Through variance analysis, it was determined that the statistical model encompasses approximately 80% of the variability in the adhesion process between materials. An improvement of up to 104% in adhesion between the materials was observed with the process, indicating an effective bond due to the presence of the thermoplastic matrix in the treated aluminum sample. This improvement is attributed to the morphology of the oxide coating, resembling corals, with micro-pores and recesses that facilitated mechanical anchoring.

A Novel Bootstrapped CMOS Switch with Minimized Sampling and Holding Error Using Sampling Window Error Analysis

This study proposes a novel 6-transistor bootstrapped switch with minimized sampling and holding error obtained through sampling window error analysis for SAR ADC design. The proposed switch design strategically mitigates channel charge injection and minimizes the input signal dependency of on-resistance by optimizing its sizing parameters. To counteract channel charge effects, dummy NMOS and PMOS components are judiciously employed, culminating in a substantial improvement in the effective number of bits (ENOB). The complete analysis of the proposed circuit is done using the Cadence Virtuoso SCL 0.18[Formula: see text][Formula: see text]m CMOS process. For a 51.514[Formula: see text]kHz sinusoidal 1[Formula: see text]V peak-to-peak differential input signal with a 1[Formula: see text]MSPs clock speed, the proposed circuit achieves 2.0141[Formula: see text]mV maximum sampling window error, 0.131[Formula: see text][Formula: see text]W power consumption, 84.67 dB signal-to-noise ratio (SNR), 84.67 signal-to-noise and distortion (SINAD) ratio and 86.02 dB spurious-free dynamic range (SFDR), which produces 13.77 bits ENOB. For the impacts of process variations and mismatch on switch performance, a comprehensive 500-point Monte Carlo (MC) simulation of the proposed bootstrap switch is conducted in this study. Post-layout results show that the proposed circuit is suitable for IoT applications.

A 2.41 ppm/°C bandgap voltage reference with second‐order curvature compensation

SummaryA current‐mode (CM) bandgap voltage reference (BGR) with second‐order curvature compensation is presented in this paper. The proposed CM BGR offers a simple compensation structure that requires only six additional transistors compared with traditional BGR designs, making it an attractive solution for low temperature coefficient (TC) voltage references. Proportional to absolute temperature (PTAT) current digital‐to‐analog converters (DACs) are used to suppress the effect of process variation and device mismatch on TC. The compensation signal is generated using the voltage difference of two bipolar junction transistor (BJT) emitter–bases whose currents are the sum and difference of the PTAT current and the zero to absolute temperature (ZTAT) current, respectively. The proposed CM BGR is designed with 0.18‐μm BIPOLAR CMOS DMOS process. A TC is 2.41 ppm/°C over a wide temperature range of −40°C to 150°C. The linear sensitivity of the supply voltage from 1.2 to 2 V is 0.027%/V. With an active area of 0.0721 mm2, it consumes 68 μA at room temperature. The integrated output noise from 0.1 to 10 Hz is 21.9 μV.

A reliable non‐volatile in‐memory computing associative memory based on spintronic neurons and synapses

AbstractThis article introduces an innovative non‐volatile associative memory (AM) that leverages spintronic synapses, employing magnetic tunnel junctions (MTJ) in conjunction with neurons constructed using carbon nanotube field‐effect transistors (CNTFETs). Our proposed design represents a significant advancement in area optimization and outperforms prior designs. We adopt MTJ‐based spintronic devices due to their remarkable attributes, including dependable reconfigurability and nonvolatility. Simultaneously, CNTFETs effectively address the longstanding limitations traditionally associated with MOSFETs. In this work, our proposed design undergoes rigorous simulations that account for process variations. The results demonstrate that our AM system closely approximates its ideal mathematical model, even with significant process variations. Furthermore, we investigate the impact of Tunnel Magnetoresistance (TMR) on the performance of our proposed AM system. Our investigations reveal that, even with a TMR as low as 100%, our design matches and often surpasses the performance of its counterparts operating with a TMR of 300%. This achievement holds profound significance from a fabrication standpoint, as fabricating MTJs with high TMR values can be intricate and costly. Overall, our novel AM system represents a significant breakthrough in emerging technologies, harnessing the unique strengths of spintronic synapses and advanced carbon nanotube transistors while robustly addressing challenges in performance and variability.

GRAPH FRACTALS WITH THE VARIABILITY OF THE FORMATION PROCESS

Fractals, which are characterized by their self-similarity at different scales, are complex geometric entities created using recursive algorithms. They are widely used in computer graphics to create complex visual effects and to model natural phenomena such as river networks and mountain landscapes. Graph fractals combine the properties of fractals and graph structures and can be used for research in fields such as computer networks or medicine. This work presents the approach of constructive-production modeling, based on formal grammars, for the generation of graph fractals with the variability of the formation process.

Estimation-based model predictive control of an electrically-heated steam methane reforming process

The surge in demand for hydrogen (H2) across diverse sectors, including clean energy transportation and chemical synthesis, underscores the need for a thorough investigation into H2 production dynamics and the development of effective controllers for industrial applications. This paper focuses on an electrically heated steam methane reforming (SMR) process for H2 production, offering advantages such as enhanced environmental sustainability, compactness, efficiency, and controllability compared to conventional reforming methods. Electric heating of the entire system allows for adjustments in current to control reactor temperature, thereby impacting hydrogen production rates. However, accurately modeling hydrogen production dynamics presents a formidable challenge, as complex models with high precision are computationally unsuitable for real-time control integration. Considering these factors, an accurate and efficient first-principles-based lumped-parameter model is developed to provide a dependable estimation of hydrogen production in an electrically-heated steam methane reformer. This model is validated experimentally and then utilized in a model predictive controller (MPC). To obtain the necessary state estimate information for the MPC, an extended Luenberger observer (ELO) method is employed to estimate state variables from limited, infrequent and delayed measurements of gas-phase reactor outlet stream and frequent measurements of the reactor temperature. Simulation comparisons with a proportional-integral (PI) controller reveal a much faster response in achieving the desired H2 production rate under the estimation-based MPC. Additionally, the simulations demonstrate the robustness of the controller to process variability such as a decrease in catalyst activation energy, commonly encountered in the SMR process, highlighting its effectiveness in maintaining stable operation under varying process conditions.

Estimating spawning Green Sturgeon (Acipenser medirostris Ayres, 1854) abundance in the Sacramento River using side-scan sonar and N-mixture models

Current estimates of the threatened southern distinct population segment of the North American Green Sturgeon (Acipenser medirostris) combine a plot-sampling density estimator with DIDSON and ARIS sonar data. From 2020 to 2022, we annually collected images of all known Green Sturgeon aggregations and compared the established method to an N-mixture model using side-scan sonar images. We compared 18 different N-mixture model combinations and chose an overdispersed Poisson model that produced estimated abundances of 742, 1286 and 1208 for 2020-2022 respectively. These numbers are ~2 times greater than the previous method and if sustained, would fulfill a key criterion for Green Sturgeon recovery. N-mixture models are known to be sensitive to violations of assumptions, such as the highly dispersed data from our study that caused serious issues, and we recommend practitioners make judicious use of overdispersion and goodness of fit tests and to be able to identify parameter confounding between detectability and abundance estimates. For our Green Sturgeon, we recommend simpler population estimates, and to focus future energy on reducing variability in the data collection process.

Diffusion Generative Model-Based Learning for Smart Layer-Wise Monitoring of Additive Manufacturing

Abstract Despite the rapid adoption of deep learning models in additive manufacturing (AM), significant quality assurance challenges continue to persist. This is further emphasized by the limited availability of sample objects for monitoring AM-fabricated builds. Thus, this study advances an emerging diffusion generative model, i.e., the denoising diffusion implicit model (DDIM), for layer-wise image augmentation and monitoring in AM. The generative model can be used to generate potential layer-wise variations, which can be further studied to understand their causation and prevent their occurrence. The proposed models integrate two proposed kernel-based distance metrics into the DDIM framework for effective layer-wise AM image augmentation. These newly proposed metrics include a modified version of the kernel inception distance (m-KID) as well as an integration of m-KID and the inception score (IS) termed KID-IS. These novel integrations demonstrate great potential for maintaining both similarity and consistency in AM layer-wise image augmentation, while simultaneously exploring possible unobserved process variations. In the case study, six different cases based on both metal-based and polymer-based fused filament fabrication (FFF) are examined. The results indicate that both the proposed DDIM/m-KID and DDIM/KID-IS models outperform the four benchmark methods, including the popular denoising diffusion probabilistic models (DDPMs), and three other generative adversarial networks (GANs). Overall, DDIM/KID-IS emerges as the best-performing model with an average KID score of 0.840, m-KID score of 0.1185, peak signal-to-noise ratio (PSNR) of 18.150, and structural similarity index measure (SSIM) of 0.173, which demonstrated strong capabilities in generating potential AM process variations in terms of layer-wise images.

Modified Levey-Jennings Chart with Robust Estimator: A Case of Semiconductor Manufacturing Process

In the era of Industrial Revolution 4.0 and smart manufacturing, the development and deployment of control charts used in the semiconductor industry need to be automated. Consequently, artificial intelligence-based automation methods typically encompass the deployment of statistical software such as JMP. Automation involves frequent dataset updates; the control limits are recalculated as the parameters change (non-stationary behaviour). This requires the user to define the control chart type before its deployment on the production floor. An initially normally distributed dataset may be skewed during the process owing to the influence of outliers. If a user selects a chart based on normality assumptions, detecting a process-mean shift may be impossible if the recalculated limit fluctuates. In the semiconductor industry, a process-mean shift occurs owing to special cause variations in the process. This signals process deterioration, which may affect the quality of the product. It is unknown when outliers will affect the equilibrium of normality assumptions; therefore, it is important to develop an automated, robust control chart that can detect special cause variations under non-stationary conditions. This study proposes the use of Huber’s M-estimators in the Levey-Jennings chart to detect special cause variations in a semiconductor manufacturing process. This study computes the robust M-estimates of all available samples to calculate the new limits in the Levey-Jennings chart. This new chart is referred to as the modified Levey-Jennings with a robust Huber M-estimator (MLVHM). Using production data from Dominant Opto Technologies Sdn. Bhd., Malaysia, a statistical comparison of the MLVHM and Levey-Jennings charts was performed. While the MLVHM is stable, the absolute difference in dispersion between the two charts ranges between 25.26% and 47.91% owing to standard deviation variation in the Levey-Jennings chart in non-stationary situations with outliers. The study concludes that the MLVHM chart is robust and suitable for industrial automatic flow applications.

Applying the Taguchi Method to Improve Key Parameters of Extrusion Vacuum-Forming Quality.

This research investigates the control of thickness and weight in plastic extrusion vacuum-thermoforming products to identify optimal key parameters for cost reduction and energy savings. The initial step involves identifying crucial influencing factors. In this step, the Delphi technique was employed through a questionnaire administered to a panel of expert scholars to ensure minimal error and maximal reliability in determining key influencing factors. Consensus was sought to establish appropriateness and consistency. Subsequently, the Taguchi method was applied for quality design and planning of the extrusion vacuum-forming process. The experimental design parameters were selected using an L18 (21 × 37) orthogonal array, and the desired quality characteristics were determined. Comparative analysis of quantitative production data from two consecutive experiments was conducted, and based on F-values and contribution analysis, the combination of control factors maximizing the Signal-to-Noise (S/N) ratio was identified. The objective is to seek optimal parameters for improving the quality of the plastic polypropylene (PP cup lid) manufacturing process, reducing process variability, and identifying the most robust production conditions. Through multiple actual production prediction experiments, it was determined that five control factors, "polypropylene new material ratio," "T-die lips adjustment thickness", "mirror wheel temperature stability", "molding vacuum pressure time", and "forming mold area design", contribute to the maximization of the S/N ratio, i.e., minimizing variability. Statistical validation confirms a significant improvement in product quality and weight control. Noteworthily, the quality control model and experimental design parameters established in this study are also applicable to other plastic products and bio-based materials, such as PET, HIPS, and biodegradable PLA lids with added calcium carbonate. The results of the experimental production demonstrate its ability to consistently control product weight within the range of 3.4 ± 0.1 g, approaching the specified tolerance limits. This capability results in approximately 2.6% cost savings in product weight, contributing significantly to achieving a company's KPI goals for environmental conservation, energy efficiency, and operational cost reduction. Therefore, the findings of this study represent a substantial and tangible contribution.

The effect of process variability and data quality on performance of a state-space stock assessment model

State-space modeling is an emerging approach to age structured fisheries stock assessment that can accommodate multiple sources of variability in processes like recruitment, abundance, and selectivity. By maximizing the marginal likelihood by treating yearly deviations as random effects and then integrating them from the likelihood, these models can estimate multiple process variances. Several fisheries software packages have been developed that use a state-space framework with marginal likelihood, which has increased their popularity and usage across the U.S. Atlantic coast, Canada, and Europe. However, robust testing is still needed to gauge the applicability of these models and understand how they perform under a range of realistic variability in the process or observation error. Using an assessment model fit to Gulf of Maine Haddock as a baseline, we used a simulation-estimation procedure to test if state-space stock assessment models could produce approximately unbiased and precise estimates over a range of process variances that extended from zero to well above the levels estimated in the Gulf of Maine Haddock assessment, or when observations were noisier (i.e., more variable around their true value) than had been assumed in the assessment. We fit alternative estimation models that differed in which processes errors were included (of recruitment, expected survival, and fishery selectivity). State-space models which specify random effects in all three processes produced approximately unbiased and precise estimates of biomass and exploitation for most operating models and therefore are recommended except when variability in expected survival is absent (or very low), in which case the model is unlikely to converge. A conventional statistical-catch-at-age model with recruitment estimated as a fixed effect for each year, deterministic expected survival, and constant selectivity produced estimates that were comparable to the best state-space model, but do not provide internal estimates of process variances and did not perform well when recruitment was highly variable. This work will facilitate the use of state-space stock assessment models and choosing the parameterization that will produce the most accurate output to inform future predictions and management.

A sequential modelling approach to determine process capability space during laser welding of high-strength Aluminium alloys

Remote laser welding (RLW) technology has become a prominent joining technology in automotive industries, offering high production throughput and cost-effectiveness. Recent advancements in RLW processes such as beam oscillation have led to an increased number of input process parameters, enabling precise control over the heat input to weld metallic materials. A critical necessity in laser welding entails selecting robust process parameters that satisfy all weld quality indicators or key performance indicators (KPIs) during two stages: production stage (often implemented as robotic welding); and repair/rework stage (implemented as cobotic/manual welding to identify process parameters for weld defects) as addressing these factors in both stages is necessary to satisfy near-zero-defect strategy for some e-mobility products.. This research presents a comprehensive methodology that encompasses the following key elements: (i) the development of physics-based simulations to establish the correlation between KPIs and process parameters; (ii) the integration of a sequential modelling approach that strikes a balance between accuracy and computation time to survey the parameter space; and (iii) development of the process capability space for the quick selection of robust process parameters.Three physical phenomena are considered in the development of numerical models, which are (i) heat transfer, (ii) fluid flow and (iii) material diffusion to investigate the effect of process parameters on the weld thermal cycle, solidification parameters and solute intermixing layer during laser welding of dissimilar high-strength aluminium alloys. The governing physical phenomena are decoupled sequentially, and KPIs are estimated based on the governing phenomena. At each step, the process capability space is defined over the parameters space based on the constraints specific to the current physical phenomena. The process capability space is determined by the constraints based on the KPIs. The process capability space provides the initial combination of process parameter space during the early design stage, which satisfies all the KPIs, thus decreasing the number of experiments. The proposed methodology provides a unique capability to (i) simulate the effect of process variation as generated by the manufacturing process, (ii) model quality requirements with multiple and coupled quality requirements, and (iii) optimise process parameters under competing quality requirements.

Ultralow Power Electronic Analog of a Biological Fitzhugh-Nagumo Neuron.

Here, we introduce an electronic circuit that mimics the functionality of a biological spiking neuron following the Fitzhugh-Nagumo (FN) model. The circuit consists of a tunnel diode that exhibits negative differential resistance (NDR) and an active inductive element implemented by a single MOSFET. The FN neuron converts a DC voltage excitation into voltage spikes analogous to biological action potentials. We predict an energy cost of 2 aJ/cycle through detailed simulation and modeling for these FN neurons. Such an FN neuron is CMOS compatible and enables ultralow power oscillatory and spiking neural network hardware. We demonstrate that FN neurons can be used for oscillator-based computing in a coupled oscillator network to form an oscillator Ising machine (OIM) that can solve computationally hard NP-complete max-cut problems while showing robustness toward process variations.

A framework for the in silico assessment of the robustness of an MPC in a CDC line in function of process variability

The shift from batch manufacturing towards continuous manufacturing for the production of oral solid dosages requires the development and implementation of process models and process control. Previous work focused mainly on developing deterministic models for the investigated system. Furthermore, the in silico tuning and analysis of a control strategy are mostly done based on deterministic models. This deterministic approach could lead to wrong actions in diversion strategies and poor transferability of the controller performance if the system behaves differently than the deterministic model. This work introduces a framework that explicitly includes the process variability which is characteristic of powder handling processes and tests it on a novel continuous feeding-blending unit (i.e., the FE continuous processing system (CPS)), followed by a tablet press (i.e., the FE 55). It employs a stochastic model by allowing the model parameters to have a probability distribution. The performance of a model predictive control (MPC), steering the feed rate of the main excipient feeder to compensate for the feed rate deviations of the active pharmaceutical ingredient (API) feeder to keep the API concentration close to the desired value, is evaluated and the impact of process variability is assessed in a Monte Carlo (MC) analysis. Next to the process variability, a model for the prediction error of the chemometric model and realistic feed rate disturbances were included to increase the transferability of the results to the real system. The obtained results show that process variability is inherently present and that wrong conclusions can be drawn if it is not taken into account in the in silico analysis.

Revealing the influence of additional structure on the flow field characteristics and flocculation performance in thickener feedwell through PIV experiments

Cemented paste backfill (CPB) is a widely used method for resource utilization of tailings. The thickener feedwell is the key equipment for CPB to realize solid-liquid separation. Flocculation is a significant process in treating slurry thickening and dehydration. To reveal the flow field variation in the flocculation process, a range of feedwells with additional structures were devised, containing the guide chute feedwell, guide shelf feedwell, and no-additional structure feedwell. The effects of hydrodynamics on flocculation performance were investigated with particle image velocimetry. The velocity and energy distribution were used to describe the variation of the flow field. The size and fractal dimension of aggregates for the flocculation reaction were studied. Furthermore, the turbidity was compared to evaluate the flocculation performance. The results indicated that additional structure can improve flocculation performance, ultimately achieving efficient solid-liquid separation. The flocculation process in feedwell incorporates the mixing-collision section and settling-growth section along the flow direction, with bounding by additional structure. The ideal high-efficiency feedwell should produce a symmetrical and uniform flow field, providing matched shear strength for two sections. The guide chute feedwell exhibited superior performance, resulting in significantly larger aggregate sizes while also experiencing remarkable compactness compared to the alternative additional structure. The reason is that tailings slurry and flocculant obtain high mixing intensity and residence time in the mixing-collision section, causing the effective collision of micro aggregates, further increasing their size and compactness in the settling-growth section. Meanwhile, gradually decaying energy distribution avoids fragmentation of aggregates. This work is expected to provide theoretical and technical support for the design and optimization of feedwells, finally achieving deep dewatering of tailings slurry, and reducing solid waste pollution in mines.

Direct comparison of different protocols to obtain surface enhanced Raman spectra of human serum

Label-free Surface Enhanced Raman Spectroscopy (SERS) is a rapid, low-invasive, and low-cost technique that has been extensively applied in clinical diagnosis and biomedicine in the study of biofluids. The purpose of this approach relies on the ability to detect specific “metabolic fingerprints” belonging to pathological biomarkers in complex biological samples, but the extremely promising potential of this technique in diagnostics is yet to be exploited, mainly because of the lack of common analytical protocols for sample preparation and analysis. Variation of experimental parameters, such as, substrate type, laser wavelength and sample processing can greatly influence spectral patterns, making results not comparable among different research groups. This study aims at pushing forward a standardization of the protocols in the analysis of human serum samples with Ag nanoparticles, starting by directly comparing the SERS spectra obtained from five different methods in which parameters like laser power, nanoparticle concentration, incubation/deproteinization steps and substrate used vary. Two protocols are the most used in the literature, and the other three are “in-house” protocols proposed by our group; all of them are employed to analyze the same human serum sample. The experimental results show that all protocols yield spectra that share the same overall spectral pattern, conveying the same biochemical information, but they significantly differ in terms of overall spectral intensity, repeatability, and preparation steps of the sample. A Principal Component Analysis (PCA) was performed revealing that protocol 3 and protocol 1 have the least variability in the dataset, while protocol 2 and 4 being the least repeatable.

An innovative damping random walk algorithm with compulsive evolution for the synthesis of heat exchanger networks

Heat exchanger network synthesis in process system engineering poses a significant challenge, primarily because of the intricacies of stream matches and the nonlinear nature of continuous variables. Numerous heuristic methods are frequently trapped in local optima because of their greedy acceptance criteria, which limits their capacity to explore diverse solutions. This aspect of enhancing algorithms through acceptance Criterion has yet to be considered in previous studies. Hence, a damping strategy using damping perfect solutions is proposed and applied to the Random Walk Algorithm with Compulsive Evolution, which has recently received considerable attention. This strategy aims to prevent trapping in local optima and improve global search capabilities, thereby reducing the risk of premature convergence to the local optima. To address the impact of different damping coefficients within the strategy on the optimization process and structural variations in the algorithm, three damping strategies were designed: a fixed damping strategy with a constant damping coefficient, a variable damping strategy featuring variable damping coefficients at different optimization stages, and an adaptive damping strategy that tracks structural changes and selectively delays modifications driven by continuous variables to preserve those driven by integer variables. The results obtained from applying these strategies to three classical industrial HEN cases are 2,890,884 $/yr, 6,650,082 $/yr, and 1,709,205 $/yr, respectively. These results outperformed the optimal outcomes documented in the literature, demonstrating the effectiveness of the new method.

Design and analysis of 7-stage MOS current mode logic power gated MOSFETs in current starved voltage-controlled oscillator for the phase locked loop application

This paper presents a new process, voltage and temperature (PVT) tolerant 7-stage ring type current starved voltage-controlled oscillator (CS-VCO). In this, a 7-stage ring VCO is proposed using power gated technique for phase locked loop (PLL) application. PLL plays a major role in clock and data recovery, Global Positioning System (GPS) system and satellite communications. For the high-speed application of PLL it is designed using 7-stage inverter delay cell with MOS current mode logic (MCML) technique. The circuit undergoes process, voltage and temperature variations with different parameters such as average power, oscillation frequency, phase noise, tuning range and output noise. The Monte-Carlo analysis justifies the proposed design provides better results. The circuit is simulated under 45 nm CMOS technology using cadence virtuoso. The average power consumption of the proposed circuit is 29.368 µW with the oscillation frequency of 3.06 GHz. The output noise and the phase noise of the proposed VCO are -161.55 dB and -125.92 dBc/Hz respectively. It achieves the frequency tuning range (FTR) of 95.09%. The obtained simulation results are highly robust with PVT making the circuit suitable for PLL application.