Fabrication Variations Research Articles

Scalable High-Precision Trimming of Photonic Resonances by Polymer Exposure to Energetic Beams.

Integrated photonic circuits (PICs) have seen an explosion in interest, through to commercialization in the past decade. Most PICs rely on sharp resonances to modulate, steer, and multiplex signals. However, the spectral characteristics of high-quality resonances are highly sensitive to small variations in fabrication and material constants, which limits their applicability. Active tuning mechanisms are commonly employed to account for such deviations, consuming energy and occupying valuable chip real estate. Readily employable, accurate, and highly scalable mechanisms to tailor the modal properties of photonic integrated circuits are urgently required. Here, we present an elegant and powerful solution to achieve this in a scalable manner during the semiconductor fabrication process using existing lithography tools: by exploiting the volume shrinkage exhibited by certain polymers to permanently modulate the waveguide's effective index. This technique enables broadband and lossless tuning with immediate applicability in wide-ranging applications in optical computing, telecommunications, and free-space optics.

Compact silicon nitride interferometers.

We demonstrate a compact silicon nitride interferometer which uses waveguides with the same length and different effective indices instead of similar effective indices and different lengths. In such structures there is no need to have waveguide bends. This not only reduces losses but also results in an order of magnitude smaller footprint and thus enables much higher integration densities. We also study the tunability of this interferometer using thermo-optical effects induced by a simple aluminum heater and show that thermal tuning can compensate for the effects of fabrication variations on the spectral response. The application of the proposed design in a tunable mirror is also briefly discussed.

Data-Driven Algorithms for Image-Based Rock Classification and Formation Evaluation in Formations With Rapid Spatial Variation in Rock Fabric

Supervised learning algorithms can be employed for the automation of time-intensive tasks, such as image-based rock classification. However, labeled data are not always available. Alternatively, unsupervised learning algorithms, which do not require labeled data, can be employed. Using either of these methods depends on the evaluated formations and the available training/input data sets. Therefore, further investigation is needed to compare the performance of both approaches. The objectives of this paper are (a) to train two supervised learning models for image-based rock classification employing image-based features from computerized tomography (CT) scan images and core photos, (b) to conduct image-based rock classification using the trained model, (c) to compare the results obtained using supervised learning models against an unsupervised learning-based workflow for rock classification, and (d) to derive class-based petrophysical models for improved estimation of petrophysical properties First, we removed non-formation visual elements from the core image data, such as induced fractures, the core barrel, and the seal peel tag on core photos. Then, we computed image-based features such as grayscale, color, and textural features from core image data and conducted feature selection. Then, we employed the extracted features for model training. Finally, we used the trained model to conduct rock classification and compared the obtained rock classes against the results obtained from an unsupervised image-based rock classification workflow. This workflow uses image-based rock fabric features coupled with a physics-based cost function for the optimization of rock classes. We applied the workflow to one well intersecting three formations with rapid spatial variation in rock fabric. We used 60% of the data to train a random forest and a support vector machines classifier using a 5-fold cross-validation approach. The remaining 40% of the data was used to test the accuracy of the supervised models. We established a base case of unsupervised learning rock classification and four different cases of supervised learning rock classification. The highest accuracy obtained for supervised rock classification was 97.4%. The accuracy obtained in the unsupervised learning rock classification approach was 82.7% when compared against expert-derived lithofacies. Class-based permeability estimates decreased the mean relative error by 34% and 35% when compared with formation-based permeability estimates, for the supervised and unsupervised approaches, respectively. The highest accuracies for the supervised and unsupervised models were obtained when integrating features from CT-scan images and core photos, highlighting the importance of feature selection for machine-learning workflows. A comparison of the two approaches for rock classification showed higher accuracy obtained from the supervised learning approach. However, the unsupervised method provided reasonable accuracy as well as a more general and faster approach for rock classification and enhanced formation evaluation.

Fiber Residual Stress Effects on Modal Gain Equalization of Few-Mode Fiber Amplifier.

The modal gain equalization (MGE) of few-mode fiber amplifiers (FMFAs) ensures the stability of signal transmission. MGE mainly relies on the multi-step refractive index (RI) and doping profile of few-mode erbium-doped fibers (FM-EDFs). However, complex RI and doping profiles lead to uncontrollable residual stress variations in fiber fabrication. Variable residual stress apparently affects MGE due to its impacts on the RI. So, this paper focuses on the residual stress effects on MGE. The residual stress distributions of passive and active FMFs were measured using a self-constructed residual stress test configuration. As the erbium doping concentration increased, the residual stress of the fiber core decreased, and the residual stress of the active fibers was two orders of magnitude lower than that of the passive fiber. Compared with the passive FMF and the FM-EDFs, the residual stress of the fiber core completely transformed from tensile stress to compressive stress. This transformation led to an obvious smooth RI curve variation. The measurement values were analyzed with FMFA theory, and the results show that the differential modal gain of the FMFA increased from 0.96 to 1.67 dB as the residual stress decreased from 4.86 to 0.01 MPa.

Computational study of mechanical stresses in a cell interacting with micromechanical cues and microfabrication of such cues in Nervous system-on-Chips

We have investigated the laser micromachining of microsieves with 3D micropore geometries. We hypothesize that mechanical cues resulting from the positioning and machining of ablated holes inside a pyramidal microcavity can influence the direction of neuronal outgrowth and instruct stem cell-derived neural networks in their differentiation processes. We narrowed the number of variations in device fabrication by developing a numerical model to estimate the stress distribution in a cell interacting with the laser-tailored unique 3D geometry of a microsieve’s pore. Our model is composed of two components: a continuous component (consisting of the membrane, cytoplasm, and nucleus) and a tensegrity structural component (consisting of the cytoskeleton, nucleoskeleton, and intermediate filaments). The final values of the mechanical properties of the components are selected after evaluating the shape of the continuous cell model when a gravity load is applied and are compared to the shape of a cell on a glass substrate after 3 h. In addition, a physical criterion implying that the cell should not slip through a hole with a bottom aperture of 3.5 μm is also set as a constraint. Among all the possible one- or multi-hole configurations, six cases appeared promising in influencing the polarization process of the cell. These configurations were selected, fabricated, and characterized using scanning electron microscopy. Fabricated microsieves consist of a 20 μm thick Norland Optical Adhesive 81 (NOA81) foil with an array of inverted pyramidal microcavities, which are opened by means of KrF 248 nm laser ablation. By changing the position of the laser beam spot on the cavities (center, slope, or corner) as well as the direction of laser beam with respect to the NOA81 microcavity foil (top side or back side), different ablation configurations yielded a variety of geometries of the 3D micropores. In the one-hole configurations when the shot is from the top side, to make the desired diameter of 3.5 μm (or less) of an opening, 1500 laser pulses are sufficient for the center and slope openings. This requirement is around 2000 laser pulses when the aperture is positioned in the corner. In back side ablation processes, the required number of pulses for through-holes at the center, slope, and corner positions are 1200, 1800, and 1800 pulses, respectively. In conclusion, we developed a microsieve platform that allows us to tailor the 3D topography of individual micropores according to the selection of cases guided by our numerical stress distribution models.

Relaxed-tolerance subwavelength grating coupler

Short-wavelength mid-infrared (MIR) silicon photonics at 2–2.5 μm wavelengths has a wide range of applications in optical communications, chemical analysis, and environmental monitoring. In this spectral region, subwavelength grating (SWG) couplers can be fabricated by using commercial multi-project wafer (MPW) services, due to their larger device feature sizes compared with those in the telecommunication band. However, conventional SWG couplers are still sensitive to variations in dimensions produced by errors in fabrication processes, restricting the fabrication reproducibility of the devices. Here, we demonstrated a relaxed-tolerance SWG coupler that has relaxed fabrication tolerance to overcome this limitation. The approach is based on the design of the SWG coupler with dual-hole structures to reduce the influence of random fabrication variations on the grating effective refractive index. Theoretically, the slope of the center wavelength shift of the relaxed-tolerance SWG coupler to the variation of the subwavelength hole size is 0.58, while that is 0.63 for the conventional SWG coupler. We demonstrated the relaxed-tolerance SWG coupler with a peak coupling efficiency of −6.2 dB at a wavelength of 2.038 μm with a 1-dB bandwidth of about 30 nm. Additionally, fabrication reproducibility and optical fiber coupling tolerance are also presented. The study paves a promising way toward developing short-wavelength MIR grating couplers with high performance, excellent repeatability, and cost-efficiency.

Broadband integrated polarization splitter and rotator using subwavelength grating claddings.

We present a broadband integrated photonic polarization splitter and rotator (PSR) using adiabatically tapered coupled waveguides with subwavelength grating (SWG) claddings. The PSR adiabatically rotates and splits the fundamental transverse-magnetic (TM0) input to the fundamental transverse-electric (TE0) mode in the coupler waveguide, while passing the TE0 input through the same waveguide. The SWGs work as an anisotropic metamaterial and facilitate modal conversions, making the PSR efficient and broadband. We rigorously present our design approaches in each section and show the SWG effect by comparing with and without the SWG claddings. The coupling coefficients in each segment explicitly show a stronger coupling effect when the SWGs are included, confirmed by the coupled-mode theory simulations. The full numerical simulation shows that the SWG-PSR operates at 1500-1750 nm (≈250 nm) wavelengths with an extinction ratio larger than 20 dB, confirmed by the experiment for the 1490-1590 nm range. The insertion losses are below 1.3 dB. Since our PSR is designed based on adiabatical mode evolution, the proposed PSR is expected to be tolerant to fabrication variations and should be broadly applicable to polarization management in photonic integrated circuits.

Influence of the Fabric Topology on the Performance of a Textile-Based Triboelectric Nanogenerator for Self-Powered Monitoring

The fibrous surface of textiles offers a larger contact area and continuous contact with the skin, which makes them a prominent candidate for biomechanical energy harvesting using the triboelectric nanogenerator principle. Herein, a lightweight, comfortable, and robust wearable triboelectric nanogenerator (W-TENG) device is fabricated for its application in self-powered technologies. The TENG output is enhanced via a three-way process: using a textured yarn and modifying the textile material and its structural variation in fabric. The triboelectric performance is correlated to the surface morphology, roughness, and dielectric properties. The current research compares woven and knitted fabric triboelectric nanogenerators using nylon, cotton, polypropylene (PP), and polyester. The surface roughness of the different textile materials and the irregular surface morphology of woven and knitted structures are studied using a three-dimensional (3D) optical profilometer. It is observed that the convolutions on the PP and nylon yarn and the 3D configuration of knitted fabric impart higher roughness than their woven counterpart providing a larger contact area. The higher contact area between the nylon/PP pair of knitted fabric has shown 240.55, 64.96, and 460% increments in voltage, current, and power density, respectively, compared to the woven W-TENG. The knitted fabric-based TENG has additionally demonstrated improved breathability and suitability to generate electrical energy from various human motions.

Simulation-based setting suggestions for yarn winding units to reduce color variation in knitted fabric

The finishing of yarns by bobbin dyeing is of key importance in the value chain of home and apparel textiles. In the bobbin dyeing process, yarn bobbins are placed on dye spindles and passed through with dye liquor. If the winding process parameters are selected unfavorably, the density of the winding package is inhomogeneous. The inhomogeneous density distribution leads to an inhomogeneous flow of dye liquid through the package and, hence, to dyeing defects. In order to minimize dyeing defects and to reduce set-up times, we present a simulation-based parameter recommendation for cross-winding machines in this paper. We use a kinematic model of the winding process combined with an empirical model for the package diameter to optimize the package density distribution. We introduce a criterion to avoid patterning defects and adapt winding settings. For bobbins with Nm 34 Co yarn, the homogeneity of the density was improved and the color deviation was reduced by up to 50% due to these simulation-based setting suggestions.

Fabrication-robust silicon photonic devices in standard sub-micron silicon-on-insulator processes.

Perturbations to the effective refractive index from nanometer-scale fabrication variations in waveguide geometry plague high index-contrast photonic platforms; this includes the ubiquitous sub-micron silicon-on-insulator (SOI) process. Such variations are particularly troublesome for phase-sensitive devices, such as interferometers and resonators, which exhibit drastic changes in performance as a result of these fabrication-induced phase errors. In this Letter, we propose and experimentally demonstrate a design methodology for dramatically reducing device sensitivity to silicon width variations. We apply this methodology to a highly phase-sensitive device, the ring-assisted Mach-Zehnder interferometer (RAMZI), and show comparable performance and footprint to state-of-the-art devices, while substantially reducing stochastic phase errors from etch variations. This decrease in sensitivity is directly realized as energy savings by significantly reducing the required corrective thermal tuning power, providing a promising path toward ultra-energy-efficient large-scale silicon photonic circuits.

Leveraging machine learning to consolidate the diversity in experimental results of perovskite solar cells.

Perovskite solar cells offer great potential for smart energy applications due to their flexibility and solution processability. However, the use of solution-based techniques has resulted in significant variations in device fabrication, leading to inconsistent results on the same composition. Machine learning (ML) and data science offer a potential solution to these challenges by enabling the automated design of perovskite solar cells. In this study, we leveraged machine learning tools to predict the band gap of hybrid organic-inorganic perovskites (HOIPs) and the power conversion efficiency of their solar cell devices. By analyzing 42 000 experimental datasets, we developed ML models for perovskite device design through a two-step predicting method, enabling the automation of perovskite materials development and device optimization. Additionally, band gap dependence of device parameters from experimental data is also validated, as predicted by the Shockley-Queisser model. This work has the potential to streamline the development of perovskite solar cells (PSCs) and optimize their performance without relying on time-consuming trial-and-error approaches.

The Effect of Fabric Construction Elements Variation on Some Mechanical and Physical Properties for Sueded Finished Fabrics

The Effect of Fabric Construction Elements Variation on Some Mechanical and Physical Properties for Sueded Finished Fabrics

A parylene-mediated plasmonic-photonic hybrid fiber-optic sensor and its instrumentation for miniaturized and self-referenced biosensing.

With the development of advanced nanofabrication techniques over the past decades, different nanostructure-based plasmonic fiber-optic sensors have been developed and have presented a low limit of detection for various biomolecules. However, owing to both the dependence on complex equipment and the trade-off between the fabrication cost and sensing performance, nanostructured plasmonic fiber-optic sensors are rarely used outside laboratories. To facilitate wider application of the plasmonic fiber-optic sensors, a parylene-mediated hybrid plasmonic-photonic cavity-based sensor was developed. Compared with a similar plasmonic sensor which only works in the plasmonic mode, the proposed hybrid sensor shows a higher reproducibility (CV < 2.5%) due to its resistance to fabrication variations. Meanwhile, a self-referenced detection mechanism and a novel miniaturized system were developed to adapt to the hybrid resonance sensor. The entire system only has a weight of 263 g, and a size of 12 cm × 10 cm × 8 cm, and is especially suitable for outdoor applications in a handheld manner. In experiments, a high refractive index sensitivity of 3.148 RIU-1 and real-time biomolecule monitoring at nanomolar concentrations were achieved by the proposed system, further confirming the potential of the miniaturized system as a candidate for point-of-care health diagnostics outside laboratories.

FDTD investigation of adiabatic optical coupling between Silicon nitride and Germanium-based optical structures for energy-efficient photonic integration

This paper aims to investigate the potential of a direct integration between Si3N4 waveguides and Germanium(Ge)-based structures, which are considered two of the most promising silicon(Si)-compatible materials for passive and active functions for Si-based energy-efficient photonics, respectively. This paper focuses on investigating the vertical coupling scheme between these materials employing a tapered structure of the Ge-based layer using 2D FDE and 3D FDTD simulations, to match the optical mode from a passive Si3N4 waveguide to the Ge-based layer and vice versa. The expected optical coupling performance will be reported, and its dependence on the unavoidable fabrication variation in the coupling region will be also investigated. Additionally, the coupling performance of the vertical coupling approach compared to the previous investigation between a Si waveguide and a Ge layer will be addressed.

Solar plasma characterization in Kappa (κ)-modified polytropic turbomagnetic GES-model perspective

ABSTRACT The gravitoelectrostatic sheath (GES) model for exploring the surface emission mechanism of the solar wind plasma (SWP), transformed from the solar interior plasma (SIP) via the solar surface boundary (SSB), has recently been reformulated in different conditions. It considers the realistic plasma key factors previously remaining unaddressed. This continued study elaborately explores the diverse GES-microphysical features influenced mainly by the plasma non-thermality, bulk polytropicity, magneto-activity, logatropicity, etc. It shows that, in the SIP (SWP), the electric potential is κ-insensitive, the Mach number is κ-sensitive, and the current density is κ-sensitive (insensitive). The Mach number is insensitive and the current density is insensitive (sensitive) to the relative temperature, Te/Ti, in the SIP (SWP). The inhomogeneity-induced GES properties are illuminated with the gradient-scale behaviours of the relevant physical variables on both SIP and SWP scales. The self-organization of the bi-scaled solar plasma system is well confirmed by mapping the electric and gravity field gradients in a defined colour phase space illustratively alongside reliability check-ups. A judicious application of the newly derived model-structuring equations results in a non-thermally modified form of the equivalent κ-Bohm sheath criterion featuring the GES formation alongside electric current-flow analyses. Its reliability is validated with a sensible comparison with the extremal cases in fair corroboration with the previous sheath scenarios extensively reported elsewhere. The relevancy of our investigation on the solar multiparametric variations in the GES fabric is lastly bolstered in light of the recent solar astronomic scenarios, such as SolO and PSP.

Compact Mid‐Infrared Chalcogenide Glass Photonic Devices Based on Robust‐Inverse Design

AbstractMid‐infrared (mid‐IR) on‐chip photonic devices have attracted increasing attention because of their potential applications in chemical and biological sensing and optical communications. In particular, chalcogenide glasses (ChGs) have long been regarded as promising materials for mid‐IR integrated photonics, owing to their broad infrared transparency, high nonlinearity, and excellent processing capabilities. Here, an inverse design approach is introduced to ChG photonic device design with a new robust inverse design method. A high‐performance mid‐IR inverse design polarization beam splitter, waveguide polarizer, mode converter, and wavelength demultiplexer are demonstrated for the first time. They all have a footprint of only several micrometers. The robust inverse design method could improve the robustness of device performance against fabrication variations and would be a general approach for designing and optimizing miniaturized chalcogenide photonic devices.

A 5.42~6.28 GHz Type-II PLL with Dead-Zone Programmability and Charge Pump Mismatch Trimming

This paper proposed a 5.42~6.28 GHz type-II phase locked loop (PLL) for the sake of both loop filter switching capability and extensive programmability. An on-chip loop filter is used in conjunction with off-chip one to form a switching filter pair for diverse application scenarios. In order to strike a balance between dead-zone elimination and noise contribution minimization, a 3-bit programmable reset time ranging from 25 ps to 200 ps with a step of 25 ps is brought into PFD (phase frequency detector) design while CP (charge pump) current is programmable from 200 μA to 900 μA with a 100 μA/step digital control. Power management units (PMU) including bandgap and low dropout regulators (LDO) are integrated on-chip with resistor string trimming which effectively counteracts fabrication variations. In addition, a piecewise linear VCO with 3-bit control is designed with a fully digital 6-bit multi-modulus divider (MMD) chain cascaded. The proposed PLL is implemented in a 40-nm bulk CMOS process and the power consumption is 8 mA@1.2 V, in which around 5 mA@1.2 V is consumed by output buffers. The fabricated PLL chip achieves a frequency tuning range of 5.42~6.28 GHz, a phase noise ranging from −107.2~−110.4 dBc/Hz@1 MHz offset from carrier, a reference spur of lower than −70 dBc when on-chip active loop filter bandwidth is set to be around 500 KHz. Its FoM is approximately −176.98~−180.18 dBc/Hz while FoMT is approximately −180.32~−183.52 dBc/Hz@1 MHz offset from carrier. Its most specifications are comparable to or better than most existing literature.

A comprehensive investigation of engineering geological characteristics of interlayer shear weakness zones embedded within Baihetan hydropower station

The interlayer shear weakness zones (ISWZs), which are a kind of large-scale infilled discontinuity, are frequently considered the most hazardous potential slip surface in the rock masses. Based on laboratory and field investigations, the engineering characteristics of ISWZs (i.e., genesis, fabric, structure, mineral composition, water-physical properties, shear strength properties, and engineering classification) embedded in and around the Baihetan hydropower station area are systematically examined.The results of the laboratory and field show that ISWZs have complex occurrence features, undulating and spatial inconsistency in thickness, and variation in mineral composition, fabrics, and structures. Moreover, based on variabilities in various features and extend, ISWZs are categorized into single-layer structures (Type-A) and multilayer structures (Type-B). An engineering geological classification approach for large-scale ISWZs is developed based on the two essential index such as clay proportion and infilled ratio. This classification method not only involves the influence of the undulating state of ISWZs on their shear properties but also considers the physical and mechanical properties of weak interlayer materials. The results of this investigation contribute to a proper understanding of mechanical properties and rock mass classification, especially for large-scale ISWZs.

A wafer-level three-step calibration technique for BJT-based CMOS temperature sensor

In order to ensure the consistency of the fabricated devices in batches, calibration is required on CMOS temperature sensor before delivery to compensate the tracking errors of the devices. In the paper, a wafer-level three-step calibration technique for BJT-based CMOS temperature sensor is proposed and fabricated based on TSMC 180-nm-BCD technology. The calibration approach is based on the wafer level, with the simplified calibration process. The strategy of the fuse burning directly maps with the proposed three-step calibration approach. The technique shows encouraging results in removing the error band of the sensor response caused by processing variations in batch fabrication. The calibrated sensor can realize an inaccuracy (±3δ) within ±0.3 °C from −40 °C to 125 °C. The temperature sensor with the calibration module in the paper has great potential application in mobile phones, battery management, disk drives and other fields, etc..

Large Dispersion Silicon Bragg Grating for Full-Field 40-GBd QPSK Phase Retrieval Receiver

Optical devices that introduce and compensate for dispersion serve an important role in communication networks, photonic signal processing, and nonlinear optics. In this article, we demonstrate a silicon photonic Bragg grating which simultaneously achieves a large dispersion and wide bandwidth. We measure the grating to have a dispersion of −146 ps/nm over a 171 GHz bandwidth, representing the highest demonstrated grating dispersion over this wide bandwidth in a standard thickness silicon photonics process. Additionally, we show a significant reduction of group delay ripple by tuning the grating using an integrated phase shifter array to address fabrication variations. We further use the silicon grating as a dispersive element in a direct-detection phase retrieval receiver for 40 GBd quadrature phase shift keying (QPSK) signals and achieve a bit error rate (BER) below the 20% forward error correction (FEC) threshold. These results establish the potential for scalable silicon photonic circuits leveraging large dispersion for optical communication and other applications.