Device Model Research Articles

A Self-Biased Triggered Dual-Direction Silicon-Controlled Rectifier Device for Low Supply Voltage Application-Specific Integrated Circuit Electrostatic Discharge Protection

A direct bidirectional current discharge path between the input/output (I/O) and ground (GND) is essential for the robust protection of charging device models (CDM) in the tightly constrained design parameters of advanced low-voltage (LV) processes. Dual-direction silicon controlled rectifiers (DDSCRs) serve as ESD protection devices with high efficiency unit area discharge, enabling bidirectional electrostatic protection. However, the high trigger voltage of conventional DDSCR makes it unsuitable for ASICs used for the preamplification of biomedical signals, which only operate at low supply voltage. To address this issue, a self-biased triggered DDSCR (STDDSCR) structure is proposed to further reduce the trigger voltage. When the ESD pulse comes, the external RC trigger circuit controls the PMOS turn-on by self-bias, and the current release path is opened in advance to reduce the trigger voltage. As the ESD pulse voltage increases, the SCR loop opens to establish positive feedback and drain the amplified current. Additionally, the junction capacitance is decreased through high-resistance epitaxy and low-concentration P-well injection to further lower the trigger voltage. The simulation results of LTspice and TCAD respectively demonstrate that ESD devices can clamp transient high voltages earlier, with low parasitic capacitance and leakage current suitable for ESD protection of high-speed ports up to 1.5 V under normal operating conditions.

Optimization of a novel lead-free MASiI3 based perovskite solar cell: A comprehensive study on device performance enhancement

Methylammonium Silicon Iodide (MASiI3), a novel perovskite material, has recently garnered significant attention for its potential to enhance the stability of solar cells. However, MASiI3-based perovskite solar cells (PSCs) have yet to achieve high performance. In this study, we utilized SCAPS-1D device modeling to investigate and optimize the performance of MASiI3-based PSCs, focusing on several key parameters. Our optimization efforts included the electron and hole transport layers (ETL and HTL), their respective thicknesses, doping densities (ND and NA), the TiO2/MASiI3 and MASiI3/Cu2O interface layers, series and shunt resistances, the metal back contact, and the influence of temperature. Through comparative analysis of various prospective materials, we identified TiO2 as the optimal ETL and Cu2O as the optimal HTL due to their superior band alignment. Our findings also highlighted the critical role of ambient temperature in maximizing power conversion efficiency (PCE). The proposed ETL and HTL materials demonstrated effective charge transport properties, contribute to enhanced device performance. Our optimized device achieved a Voc of 0.896 V, a Jsc of 42.47 mA/cm2, an FF of 76.94 %, and a remarkable PCE of 29.29 %. These results underscore the potential of MASiI3-based PSCs when utilizing TiO2 and Cu2O as charge-carrying materials and pave the way for further advancements in perovskite solar cell technology.

Analysis and Research on Energy Consumption of a Non-Contact High-Efficiency Tunnel De-Icing Device

In this paper, a laser deicing device for tunnel is designed to solve the inconvenience of deicing the tunnel roof wall. First of all, Solidworks software was used to create a 3D model of the laser deicing device, namely the track module, the base platform, and the work platform. Then, ANSYS simulation software is used to simulate the process of laser deicing. When the initial temperature of icicle is -5℃, -10℃, -15℃ and -20℃, the distance between laser and icicle is 1.5m, 2m, 2.5m and 3m, and the wind speed is 2m/s, 3m/s, 4m/s and 5m/s, respectively. The depth of 200s of laser irradiation and the time of full penetration were simulated under three variables. It is concluded that the higher the initial temperature, the closer the distance and the smaller the wind speed, the lower the deicing energy consumption. Finally, the experiment results are consistent with the simulation results, which further verifies the accuracy of the simulation. The laser deicing detection device studied in this paper provides a certain theoretical basis and reference value for the application and development of intelligent deicing equipment in tunnels.

Hardware requirements for trapped-ion-based verifiable blind quantum computing with a measurement-only client

Abstract In blind quantum computing (BQC), a user with a simple client device can perform a quantum computation on a remote quantum server such that the server cannot gain knowledge about the computation. Here, we numerically investigate hardware requirements for verifiable BQC using an ion trap as server and a distant measurement-only client. While the client has no direct access to quantum-computing resources, it can remotely execute quantum programs on the server by measuring photons emitted by the trapped ion. We introduce a numerical model for trapped-ion quantum devices in NetSquid, a discrete-event simulator for quantum networks. Using this, we determine the minimal hardware requirements on a per-parameter basis to perform the verifiable BQC protocol. We benchmark these for a five-qubit linear graph state, with which any single-qubit rotation can be performed, where client and server are separated by 50 km. Current state-of-the-art ion traps satisfy the minimal requirements on a per-parameter basis, but all current imperfections combined make it impossible to perform the blind computation securely over 50 km using existing technology. Using a genetic algorithm, we determine the set of hardware parameters that minimises the total improvements required, finding directions along which to improve hardware to reach our threshold error probability that would enable experimental demonstration. In this way, we lay a path for the near-term experimental progress required to realise the implementation of verifiable BQC over a 50 km distance.

Modeling of Metal Matrix Concentrated Solar Receivers for sCO2 Power Blocks

Abstract Supercritical CO2 power blocks for Concentrating Solar Power (CSP) with novel metal matrix solar receivers have the potential to reduce operating expenses while improving overall system efficiencies. These concentrated solar receivers integrated with a metal matrix-based phase change (PCM) thermal storage medium provide the compounding effect of an efficient heat exchanger while also integrating thermal storage within the receiver. Detailed numerical modeling of such devices with enthalpy-porosity-based formulation for phase change and turbulent convective heat transfer for the sCO2 microchannels is described in the current work. With sCO2 power blocks operating at temperatures and pressures beyond 800°C and 200 bar, different high-temperature PCMs are studied. A detailed steady charging followed by discharge and charging transients is simulated to analyze the thermal performance of these devices. In the current work, the energy storage density of PCMs is studied, followed by an analysis of the movement of the melt-pool interface over the streamwise length of a high corrugation wavy microchannel. A scaling analysis is carried out to estimate the heat transfer coefficients while compare them with the numerical model predictions. The results from the current work can be utilized for sizing and detailed design of integrated solar receivers for high-temperature sCO2 power block applications.

Exhaust Gas Flow Study of Electric Turbo Compounding (ETC) to Determine the Potential Electrical Energy Recovery from Exhaust Emission

Electric turbo compounding (ETC) emerges as a pivotal energy recovery solution, seamlessly combining an advanced turbocharger with a high-speed generator to harvest electrical energy from exhaust gas dynamics. This research delves into the practical application of ETC in vehicles, employing a comprehensive approach blending simulation techniques with experimental validation. The investigative process involves meticulous data collection on vehicle muffler dimensions, subsequent device modelling based on this data, and rigorous flow simulations, followed by thorough analysis. The study's key findings highlight a noteworthy 9% increase in engine back pressure at 850 rpm, counterbalanced by a 7% reduction at 2000 rpm. Despite the integration of the ETC mechanism, electric current measurements remain consistently within the range of 1.4 to 1.6 amperes at 1200-2000 rpm. This research not only unveils the tangible effects of ETC on engine performance but also underscores its viability as an efficient energy recovery solution in the automotive sector, setting the stage for advancements in sustainable transportation.

Study of the fuel irradiation capsule using NEPTUNE_CFD software

The Fuel Irradiation Capsule (FUICA) is an experimental device designed to irradiate fuel elements in a Material Testing Reactor (MTR) during long period. This component is being studied for implementation in the Jules Horowitz reactor (RJH) under construction in France (1).Its function is to irradiate and to cool fuel sample on a passive way by transferring the thermal energy generated to the device periphery, through an annular zone of confined and pressure-regulated water (2). Its external wall, kept cold by the reactor cooling circuit, allows for the heat extraction. Natural convection is the driving force for the heat transfer between the fuel element and the cold wall of the device. By eliminating any internal forced convection cooling system, the device becomes relatively simple and cost-effective.This paper describes the device modeling carried out using the NEPTUNE_CFD software (3). The present work, in its first part, focuses on the behavior of the device depending on the heat flux, and in its second part, the sensitivity to boundary conditions. The objective is to provide a way to design and optimize the fuel capsule thermal characteristics. For that, an arbitrary size of fuel capsule is chosen in order to analyze the physics involved. A sensitivity study is then presented in order to highlight the influence of the physical parameters on the fuel capsule performance.Three operating ranges for the heat transfer are exhibited in this study: moderate in the liquid phase, the heat transfer becomes optimal in the two-phase regime, and even improves with the level of heat flux before deteriorating abruptly for high flux called Critical Heat Flux (CHF). Two observations also arise to improve thermal transfer: in the liquid phase, it is necessary to reduce the water thickness to improve convection, and in the two-phase regime, the heat transfer increases proportionally to the heat flux. In the end, the irradiation capsule is capable of extracting very high heat flux.

Multiscale Modeling of Plasma-Assisted Non-Premixed Microcombustion

This work explores microcombustion technologies enhanced by plasma-assisted combustion, focusing on a novel simulation model for a Y-shaped device with a non-premixed hydrogen-air mixture. The simulation integrates the ZDPlasKin toolbox to determine plasma-produced species concentrations to Particle-In-Cell with Monte Carlo Collision analysis for momentum and power density effects. The study details an FE-DBD plasma actuator operating under a sinusoidal voltage from 150 to 325 V peak-to-peak and a 162.5 V DC bias. At potentials below 250 V, no hydrogen dissociation occurs. The equivalence ratio fitting curve for radical species is incorporated into the plasma domain, ensuring local composition accuracy. Among the main radical species produced, H reaches a maximum mass fraction of 8% and OH reaches 1%. For an equivalence ratio of 0.5, the maximum temperature reached 2238 K due to kinetic and joule heating contributions. With plasma actuation with radicals in play, the temperature increased to 2832 K, and with complete plasma actuation, it further rose to 2918.45 K. Without plasma actuation, the temperature remained at 300 K, reflecting ambient conditions and no combustion phenomena. At lower equivalence ratios, temperatures in the plasma area consistently remained around 2900 K. With reduced thermal power, the flame region decreased, and at Φ = 0.1, the hot region was confined primarily to the plasma area, indicating a potential blow-off limit. The model aligns with experimental data and introduces relevant functionalities for modeling plasma interactions within microcombustors, providing a foundation for future validation and numerical models in plasma-assisted microcombustion applications.

An optimal load distribution and real-time control strategy for integrated energy system based on nonlinear model predictive control

Efficient operational scheduling and control are vital for the operation process of the Integrated Energy System (IES), especially under varying operational conditions. An optimal load distribution and real-time control strategy for IES based on Nonlinear Model Predictive Control has been developed to synergistically improve equipment output performance and reduce operating costs. In the proposed strategy, the IES model that integrates linear parameter-varying models for devices and dynamic models for pipe networks, has been constructed via system identification methodology under varying operational conditions. Thus, a hierarchical operation and control framework is established using the model predictive control strategy, including real-time prediction objective, optimal load distribution objective, and optimization algorithms. Consequently, a case study is performed to investigate the supply-demand balance and economic benefit of the proposed strategy. From the aspect of supply-demand balance, the average energy supply-demand discrepancy by using the proposed strategy (for cooling and heating) is approximately 25 %∼33 % that under the steady-state strategy. By means of the proposed strategy, an outstanding equipment output performance will be achieved and supply-demand discrepancy can be dramatically reduced. From the perspective of economic performance, the proposed strategy, which fully considers the nonlinearity of devices, exhibits a 16 % decrease in the operational cost compared to that under the linear dynamic strategy. Through the proposed strategy, efficiency losses are expected to degrade with an excellent economic benefit. Therefore, the suggested strategy is promising and suitable for varying operational condition scenarios.

Device Modeling of 4H-SiC PIN Photodiodes with Shallow Implanted Al Emitters for VUV Sensor Applications

A numerical model is presented for the simulation of ultraviolet ion-implanted 4H-SiC photodiodes with shallow p- emitter doping profiles. An existing model for SiC pin photodiodes, taken from literature, is modified with a dedicated SiO2-SiC interface layer to account for degradation of carrier mobility and lifetime at the interface. Furthermore, aluminum compensation in 4H-SiC is included and its impact on the spectral response and carrier recombination is analyzed. The simulated spectral response in the wavelength range from 200 to 400 nm is compared to experimental data. While the existing model, taken from literature, fails to predict the performance of VUV photodiodes with a shallow p- emitter, the newly designed model successfully achieves high accuracy, even with a basic modeling approach featuring an abrupt material parameter transition.

Algorithm prediction of single particle irradiation effect based on novel TFETs

In order to predict the single particle irradiation of tunnel field effect transistor (TFET) devices, a deep learning algorithm network model was built to predict the key characterization parameters of the single particle transient. Computer aided design (TCAD) technique is used to study the influence of single particle effect on the novel stacked source trench gate TFET device. The results show that with the increase of drain voltage, incident width of heavy ions (less than 0.04μm), and linear energy transfer, the drain transient current and collected charge also increase. The prediction results of deep learning algorithm show that the relative error percentage of drain current pulse peak (IDMAX) and collected charge (Qc) is less than 10%, and the relative error percentage of most predicted values is less than 1%. Comparison experiments with five traditional machine learning methods (support vector machine, decision tree, K-nearest algorithm, ridge regression, linear regression) show that the deep learning algorithm has the best performance and has the smallest average error percentage. This data-driven deep learning algorithm model not only enables researchers who are not familiar with semiconductor devices to quickly obtain the transient data of a single particle under any conditions; at the same time, it can be applied to digital circuit design as a data-driven device model reflecting the reliability of single particle transient. The application of deep learning in the field of device irradiation prediction has a highly promising prospect in the future.

Implementing microfluidic flow device model in utilizing dural substitutes as pulp capping materials for vital pulp therapy

Vital pulp therapy (VPT) has gained prominence with the increasing trends towards conservative dental treatment with specific indications for preserving tooth vitality by selectively removing the inflamed tissue instead of the entire dental pulp. Although VPT has shown high success rates in long-term follow-up, adverse effects have been reported due to the calcification of tooth canals by mineral trioxide aggregates, which are commonly used in VPT. Canal calcification poses challenges for accessing instruments during retreatment procedures. To address this issue, this study evaluated the mechanical properties of dural substitute intended to alleviate intra-pulp pressure caused by inflammation, along with assessing the biological responses of human dental pulp stem cells (hDPSC) and human umbilical vein endothelial cells (HUVEC), both of which play crucial roles in dental pulp. The study examined the application of dural substitutes as pulp capping materials, replacing mineral trioxide aggregate (MTA). This assessment was conducted using a microfluidic flow device model that replicated the blood flow environment within the dental pulp. Computational fluid dynamics simulations were employed to ensure that the fluid flow velocity within the microfluidic flow device matched the actual blood flow velocity within the dental pulp. Furthermore, the dural substitutes (Biodesign; BD and Neuro-Patch; NP) exhibited resistance to penetration by 2-hydroxypropyl methacrylate (HEMA) released from the upper restorative materials and bonding agents. Finally, while MTA increased the expression of angiogenesis-related and hard tissue-related genes in HUVEC and hDPSCS, respectively, BD and NP did not alter gene expression and preserved the original characteristics of both cell types. Hence, dural substitutes have emerged as promising alternatives for VPT owing to their resistance to HEMA penetration and the maintenance of stemness. Moreover, the microfluidic flow device model closely replicated the cellular responses observed in live pulp chambers, thereby indicating its potential use as an in vivo testing platform.

SILAR deposition and characterization of ZnO films for numerical investigation as electron transport layer in solar cells

The SILAR method of thin film deposition has attracted the scientific community over the years due to its easiness, low cost, availability of room temperature deposition, and more over due to the variation in properties of thin films available by varying deposition parameters.This work is carried out in a way to comprehensively compare two ZnO thin film samples prepared from precursor media with Zinc Acetate (S1) and Zinc Chloride(S2) salts deposited by SILAR method in Perovskite Solar cell applications. The XRD, FTIR, Raman, FESEM, and UV-Visible analysis were carried out for identifying the structural, morphological, and optical quality of these samples. The role of these samples as Electron Transport Layer (ETL) in Perovskite Solar cell were identified using General purpose PhotoVoltaic Device model (GPVDM) simulation software which is well adapted for studying Solar cell architecture. It provided the output Solar cell parameters like Jsc, Voc, FF, PCE, etc and by varying the active layer and Hole Transport Layer (HTL) thicknesses, the optimized efficiency of devices with samples S1 and S2 were obtained as 21.88% and 21.96%.The results showed that SILAR-synthesized ZnO thin films could be potential candidates for ETL applications in Perovskite Solar cells.

Numerical analysis and device modelling of a lead-free Sr3PI3/Sr3SbI3 double absorber solar cell for enhanced efficiency.

Halide perovskites are the most promising options for extremely efficient solar absorbers in the field of photovoltaic (PV) technology because of their remarkable optical qualities, increased efficiency, lightweight design, and affordability. This work examines the analysis of a dual-absorber solar device that uses Sr3SbI3 as the bottom absorber layer and Sr3PI3 as the top absorber layer of an inorganic perovskite through the SCAPS-1D platform. The device architecture includes ZnSe as the electron transport layer (ETL), while the active layer consists of Sr3PI3 and Sr3SbI3 with precise bandgap values. The bandgap value of Sr3SbI3 is 1.307 eV and Sr3PI3 is 1.258 eV. By employing double-graded materials of Sr3PI3/Sr3SbI3, the study achieves an optimized efficiency of up to 34.13% with a V OC of 1.09 V, FF of 87.29%, and J SC of 35.61 mA cm-2. The simulation explores the influence of absorber layer thickness, doping level, and defect density on electrical properties like efficiency, short-circuit current, open-circuit voltage, and fill factor. It also examines variations in temperature and assesses series and shunt resistances in addition to electrical factors. The simulation's output offers valuable insights and suggestions for designing and developing double-absorber solar cells.

Toward trustworthy medical device in silico clinical trials: a hierarchical framework for establishing credibility and strategies for overcoming key challenges.

Computational models of patients and medical devices can be combined to perform an in silico clinical trial (ISCT) to investigate questions related to device safety and/or effectiveness across the total product life cycle. ISCTs can potentially accelerate product development by more quickly informing device design and testing or they could be used to refine, reduce, or in some cases to completely replace human subjects in a clinical trial. There are numerous potential benefits of ISCTs. An important caveat, however, is that an ISCT is a virtual representation of the real world that has to be shown to be credible before being relied upon to make decisions that have the potential to cause patient harm. There are many challenges to establishing ISCT credibility. ISCTs can integrate many different submodels that potentially use different modeling types (e.g., physics-based, data-driven, rule-based) that necessitate different strategies and approaches for generating credibility evidence. ISCT submodels can include those for the medical device, the patient, the interaction of the device and patient, generating virtual patients, clinical decision making and simulating an intervention (e.g., device implantation), and translating acute physics-based simulation outputs to health-related clinical outcomes (e.g., device safety and/or effectiveness endpoints). Establishing the credibility of each ISCT submodel is challenging, but is nonetheless important because inaccurate output from a single submodel could potentially compromise the credibility of the entire ISCT. The objective of this study is to begin addressing some of these challenges and to identify general strategies for establishing ISCT credibility. Most notably, we propose a hierarchical approach for assessing the credibility of an ISCT that involves systematically gathering credibility evidence for each ISCT submodel in isolation before demonstrating credibility of the full ISCT. Also, following FDA Guidance for assessing computational model credibility, we provide suggestions for ways to clearly describe each of the ISCT submodels and the full ISCT, discuss considerations for performing an ISCT model risk assessment, identify common challenges to demonstrating ISCT credibility, and present strategies for addressing these challenges using our proposed hierarchical approach. Finally, in the Appendix we illustrate the many concepts described here using a hypothetical ISCT example.

Power system reliability assessment technique and modeling approach based on quantum computing theory

Aiming at the reliability assessment of power system, the state sampling process as a critical step requires to generate power system states one by one using classical computer. Due to the quantum superposition, all system states can be generated at once using the quantum computing and then assess the reliability, and thus it will improve the assessment efficiency. However, how to sample system states and assess reliability using the quantum computing theory will be very important. Therefore, this paper proposes a power system reliability assessment technique and its modeling approach based on the quantum computing theory. Firstly, this paper develops a quantum model of power system reliability (including reliability models of electrical device and system) and proposes a new system state sampling method based on the quantum computing theory, which samples all system states at once and improves the sampling efficiency. Secondly, this paper proposes a quantum computing theory based on reliability assessment technique and its modeling approach for the reliability indices: Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS). Thirdly, this paper uses an iterative quantum amplitude estimation (IQAE) algorithm to measure the reliability indices for improving the measurement accuracy. Finally, this paper investigates both designed Case and RBTS, and results shows the correctness and effectiveness of reliability assessment of power system based on the quantum computing.

A comprehensive survey of deep learning-based lightweight object detection models for edge devices

This study concentrates on deep learning-based lightweight object detection models on edge devices. Designing such lightweight object recognition models is more difficult than ever due to the growing demand for accurate, quick, and low-latency models for various edge devices. The most recent deep learning-based lightweight object detection methods are comprehensively described in this work. Information on the lightweight backbone architectures used by these object detectors has been listed. The training and inference processes concerning to deep learning applications on edge devices is being discussed. To raise readers’ awareness of this developing domain, a variety of applications for deep learning-based lightweight object detectors and related utilities have been offered. Designing potent, lightweight object detectors based on deep learning has been suggested as a counter to such problems. On well-known datasets such as MS-COCO and PASCAL-VOC, we thoroughly examine the performance of certain conventional deep learning-based lightweight object detectors.

Transport Effects in Electrochemical Biochip Sensors with Redox Cycling Amplification: Computational and Experimental Analysis

In the last few decades, and especially after the COVID-19 pandemic, point-of-care diagnostics and field-deployable biosensors have attracted significant attention. Key components of these instruments are microchips that integrate microfluidics with a sensor (e.g., optical or electrochemical) to provide rapid, low-cost, real-time detection of analyte molecules in very small volumes. Next-generation electrochemical sensors have been miniaturized on solid-state platforms (e.g., arrays and vectors) where the area is limited. However, as the active surface area of the sensors decreases from a few square microns to sub-micron and nanometric scales, the measured currents drop, hence affecting the limit of detection. As the concentration of many analytes in the physiological environment is low, low signal-to-noise ratios have limited the miniaturization of standard electrochemical sensors. One way to increase signal at the sensor output is by redox amplification. To achieve amplification, a second working electrode must be incorporated close to the first working electrode and proper bias must be applied to both. Micrometer-sized interdigitated electrode arrays have been fabricated using robust and scalable processes based on optical lithography and reactive ion-etching techniques. In some cases, IDAs have been integrated into microfluidics devices. The physical and electrochemical phenomena that occur in microfluidic channels under flow have been previously evaluated and showed an increase of current under flow. The effect of redox cycling in microfluidic and nanofluidic systems has also been qualitatively demonstrated. Some studies measured the amplification in the cases with and without flow and showed that the flow itself amplifies the measured electrochemical current, though in most cases, it reduces the amplification factor.Towards a more quantitative investigation, analytical and computational modeling were used, providing meaningful insights into the physics of IDAs without requiring numerous device fabrications. In this work, we used COMSOL Multiphysics to create a full three-dimensional simulation-based model of a real device in order to study the effect of laminar flow on redox cycling, as well as a simplified one-dimensional model for the analysis of transition velocity between different amplification regimes. IDAs with 5-10 mm for both electrodes width and gap were studied numerically and experimentally. The results of the 3D and 1D simulations were consistent between themselves, and also with those of the physical experiments. The relationship between the two mass transfer methods — diffusion and convection — were analyzed at different flow velocities and for different electrode geometries.Here we report a study of transport effects on a microfluidic sensor-chip in which electrochemical sensing is amplified by redox cycling using an interdigitated electrode array (IDA). Our models indicate that there exist two dominant regimes: one which is limited by redox cycling diffusion—a unique feature of IDAs— which is dominant at relatively low flow rates, and another which is limited by convection and has dominant flow effects which are effective at high flow rates (see Figure). The transition velocity between the two sensing regimes depends on the width of the electrode and spacing. It was found to be Vflow [mm/sec]=0.335/L[μm] + 0.04 which is similar to the prediction of Vflow∝1/L obtained from the one-dimensional model.An understanding of the combined effects of redox cycling and convection flow is critical as microfluidic chips with electrochemical sensing amplified by redox cycling using IDAs are becoming widely used in diagnostic assays. The method for prediction of current under any flow rate described here will support technological development of more efficient and accurate devices. Figure 1

Spatial Nonuniformity of Photoresponse in SiC UV Avalanche Photodiodes Operating in Linear and Geiger Mode

Silicon Carbide's intrinsic material properties, along with the availability of low-defect density, 6" substrates, make it an attractive material system for visible-blind ultraviolet (UV) detectors. Avalanche photodiodes (APDs) made from SiC have been shown to operate both as linear mode detectors with high gain (>10⁷) and as single-photon avalanche detectors (SPADs) with competitive photon detection efficiency. However, reported spatial non-uniformities in photoresponse of these devices will reduce sensitivity [1,2].In this paper, we examine the impact of device geometry, epitaxial variations, and crystal anisotropy on the spatial uniformity of photoresponse in SiC APDs. Photoresponse maps are generated using a fast 285 nm pulsed LED source that is tightly focused to a ~10 µm spot. Response maps are measured for devices with different geometries fabricated on the same chip in both linear-mode and Geiger-mode. Measurements on standard pin diodes will be presented, along with separate absorption, charge, and multiplication (SACM) structures.Experimental results indicate that all devices exhibit localized regions of high photoresponse at gain > 1000 and that the spreading of the photoresponse is highly directional. Finger diodes with 120 μm x 30 μm mesas exhibit good response uniformity with a variation < 30% across the device. In contrast, diodes with 60 and 90 μm square-shaped mesas exhibit similar uniform response spreading along one edge that is bounded by the size of the mesa, but a >10x drop in relative response over ~30-40 μm in the orthogonal direction which is much shorter than the extent of the mesa. Devices with 100 μm circular mesas exhibit somewhat similar results to the square diodes, but with the presence of multiple hot spots in response.Experimental results are analyzed through 3D numerical modeling of devices which accounts for the effects of doping and thickness variations across the substrate as well as the anisotropy of impact ionization. Modeling indicates that small variations in the thickness in the drift regions of a diode can yield meaningful variations in avalanche gain across the mesa. The impact of anisotropic impact ionization will be considered using full-band 3D Monte Carlo calculations. X. Bai, H.-D. Liu, D. C. McIntosh and J. C. Campbell, "High-Detectivity and High-Single-Photon-Detection-Efficiency 4H-SiC Avalanche Photodiodes," IEEE J. Quantum Electron. 45 300-303 (2009). DOI: 10.1109/JQE.2009.2013093.L. Su et al, "Recent progress of SiC UV single photon counting avalanche photodiodes," J. Semicond. 40 121802 (2019). DOI: 10.1088/1674-4926/40/12/121802. Figure 1

(Invited) Device Engineering of Organic Electro-Chemical Transistors and Solar Cells

Traditionally, new material-based technologies evolve first through chemistry and physics, with device engineering assuming a key role only towards commercialization. The device structure of an OLED pixel in any screen is a testament to the contribution of engineering to the challenge of achieving efficient and stable devices. In the talk, I will discuss two fields where this is yet to happen. The first is the organic electrochemical transistors, and the second is the solution-processed organic solar cells. Using both a detailed 2D chemical-physics semiconductor device model and a rate-equation level model, I will delve into the physics and electrochemistry of OECTs showing the role of electrode reactions and counter ions. In the context of OPVs, I will show that focusing on the performance at the maximum power point, instead of the short and open circuit, allows the design where the device contributes to charge generation and extraction.