Explore the vast range of titles available.

\"Features 10 advanced electronics projects to build with the Arduino platform, including a garage alarm, a pH meter, and an automatic wristwatch winder. Each project includes a template for creating a circuit board, and tips for using power tools and other building materials\"-- Provided by publisher.

ULRICH L. ROHDE, PhD, Dr.-ing habil., is Chairman of Synergy Microwave Corporation; a partner of Rohde & Schwarz; and Professor of Microwave and RF Technology at the Brandenburgische Technische Universität Cottbus, Germany. He is a Fellow of the IEEE. MATTHIAS RUDOLPH, PhD, Dr.-ing, is the Ulrich L. Rohde Professor for RF and Microwave Techniques at Brandenburgische Technische Universität Cottbus, Germany. He worked previously at the Ferdinand-Braun-Institut, Leibniz-Institut für Höchstfrequenztechnik (FBH) in charge of GaAs semiconductor nonlinearity and noise modeling.

Microfluidic systems are rapidly becoming commonplace tools for high-precision materials synthesis, biochemical sample preparation, and biophysical analysis. Typically, microfluidic systems are constructed in monolithic form by means of microfabrication and, increasingly, by additive techniques. These methods restrict the design and assembly of truly complex systems by placing unnecessary emphasis on complete functional integration of operational elements in a planar environment. Here, we present a solution based on discrete elements that liberates designers to build large-scale microfluidic systems in three dimensions that are modular, diverse, and predictable by simple network analysis techniques. We develop a sample library of standardized components and connectors manufactured using stereolithography. We predict and validate the flow characteristics of these individual components to design and construct a tunable concentration gradient generator with a scalable number of parallel outputs. We show that these systems are rapidly reconfigurable by constructing three variations of a device for generating monodisperse microdroplets in two distinct size regimes and in a high-throughput mode by simple replacement of emulsifier subcircuits. Finally, we demonstrate the capability for active process monitoring by constructing an optical sensing element for detecting water droplets in a fluorocarbon stream and quantifying their size and frequency. By moving away from large-scale integration toward standardized discrete elements, we demonstrate the potential to reduce the practice of designing and assembling complex 3D microfluidic circuits to a methodology comparable to that found in the electronics industry. Significance Microfluidic systems promise to improve the analysis and synthesis of materials, biological or otherwise, by lowering the required volume of fluid samples, offering a tightly controlled fluid-handling environment, and simultaneously integrating various chemical processes. To build these systems, designers depend on microfabrication techniques that restrict them to arranging their designs in two dimensions and completely fabricating their design in a single step. This study introduces modular, reconfigurable components containing fluidic and sensor elements adaptable to many different microfluidic circuits. These elements can be assembled to allow for 3D routing of channels. This assembly approach allows for the application of network analysis techniques like those used in classical electronic circuit design, facilitating the straightforward design of predictable flow systems.

The ability to design and fabricate lightweight structure is one of the most important aspects for building a drone system. Often, connecting wires are used on the drone system for powering and signal transmission at the expense of the drone’s weight. In this paper, we explore the design and fabrication of a safety cage for drone using design optimization and 3D printing. A comparison between fused deposition modelling (FDM) and selective laser sintering (SLS) 3D printing techniques for the fabrication of thin structures was made, and it was found that SLS is more superior in this aspect. A hybrid 3D printing process combining SLS and aerosol jet printing is proposed for the fabrication of lightweight multifunctional structure with printed circuit for a drone safety cage. The safety cage is designed in such a way that maximizes the production efficiency of SLS printing process. In addition, aerosol jet printing is used to fabricate conformal circuit onto the 3D-printed safety cage structure to replace the conventional connecting wires for weight saving purpose. Lastly, an electrical characterization is conducted to investigate the functionality of the printed conductive traces on the safety cage. Nevertheless, this work demonstrates the streamlining of various 3D printing approaches for the fabrication of multifunctional structures with conformal circuits.

Analog integrated circuit design is widely considered a time-consuming task due to the acute dependence of analog performance on the transistors’ and passives’ dimensions. An important research effort has been conducted in the past decade to reduce the front-end design cycles of analog circuits by means of various automation approaches. On the other hand, the significant progress in high-performance computing hardware has made machine learning an attractive and accessible solution for everyone. The objectives of this paper were: (1) to provide a comprehensive overview of the existing state-of-the-art machine learning techniques used in analog circuit sizing and analyze their effectiveness in achieving the desired goals; (2) to point out the remaining open challenges, as well as the most relevant research directions to be explored. Finally, the different analog circuits on which machine learning techniques were applied are also presented and their results discussed from a circuit designer perspective.

In this paper, we present an artificial neural network (ANN)-based compact model to evaluate the characteristics of a nanosheet field-effect transistor (NSFET), which has been highlighted as a next-generation nano-device. To extract data reflecting the accurate physical characteristics of NSFETs, the Sentaurus TCAD (technology computer-aided design) simulator was used. The proposed ANN model accurately and efficiently predicts currents and capacitances of devices using the five proposed key geometric parameters and two voltage biases. A variety of experiments were carried out in order to create a powerful ANN-based compact model using a large amount of data up to the sub-3-nm node. In addition, the activation function, physics-augmented loss function, ANN structure, and preprocessing methods were used for effective and efficient ANN learning. The proposed model was implemented in Verilog-A. Both a global device model and a single-device model were developed, and their accuracy and speed were compared to those of the existing compact model. The proposed ANN-based compact model simulates device characteristics and circuit performances with high accuracy and speed. This is the first time that a machine learning (ML)-based compact model has been demonstrated to be several times faster than the existing compact model.

Two-dimensional transition metal dichalcogenides could potentially be used to create transistors that are scaled beyond the capabilities of silicon devices. However, despite progress on the single-transistor level, the development of high-frequency integrated circuits remains a challenge and the operating frequency of integrated circuits based on transition metal dichalcogenides has so far been limited to the megahertz regime; this is well below the silicon complementary metal–oxide–semiconductor technology, as well as emerging technologies such as carbon nanotubes. Here we report two-dimensional semiconductor integrated circuits—five-stage ring oscillators—that operate in the gigahertz regime (up to 2.65 GHz) and are developed using a design-technology co-optimization process. The circuits are based on monolayer molybdenum disulfide field-effect transistors that have an air-gap structure, which leads to doping-free ohmic contacts and low parasitic capacitance. Technology computer-aided design simulations also suggest that our air-gap structure can potentially be scaled to the 1 nm technology node and could reach the targets set out in the IEEE International Roadmap for Devices and Systems for 2031. Five-stage ring oscillators that operate at frequencies of up to 2.65 GHz can be created using monolayer molybdenum disulfide field-effect transistors developed with a design-technology co-optimization process.

In microwave device and circuit design, many simulations are often needed to find a set of designs that satisfy one or multiple specifications chosen by the designer upfront: the feasible region. A novel Bayesian active learning framework is presented to accurately identify the feasible region with a low number of simulations. The technique leverages on a stochastic model to obtain an efficient and automated procedure. A suitable application example validates the proposed technique and shows its effectiveness to rapidly obtain many suitable designs.

Diagnosis of analog electronic circuits is a crucial issue in computer-aided design. During the diagnosis, solving a test equation to identify the values of faulty parameters is usually necessary. The equation is nonlinear to the parameters, even for linear circuits. The nonlinearity of the equation implies the possibility of multiple solutions. No method exists that guarantees the determination of all the solutions of the test equation. However, even information about more than one existing solution is essential for the designer. It allows for the selection of another test at the design step and helps to obtain an unambiguous solution during the diagnosis. Information about the possibility of additional solutions is essential for simulation after test methods (e.g., identification and verification methods) and for simulation before test methods, so-called dictionary methods, especially those targeting multiple fault classification. The paper deals with the problem of multiple solutions of the test equation for nonlinear DC circuits and proposes a method for identifying the solutions using a deflation technique. The outcomes are compared with the results obtained using standard and adaptively damped Newton–Raphson iterative methods. The methods use randomly selected initial guesses to find multiple solutions. The effectiveness of all the methods for identifying multiple solutions was verified numerically and via laboratory tests.

This study aims to implement universal logic gates using polarity control within a single silicon transistor structure. For this purpose, a reconfigurable transistor based on a p-i-n structure featuring two polarity gates (PGs) and one control gate was proposed, and its electrical characteristics and logic-in-memory (LIM) circuit operations were analyzed via two-dimensional technology computer-aided design simulations. The proposed device could be perfectly reconfigured into p-channel or n-channel modes because virtual doping effects could be induced according to the polarity of the PG voltage. Moreover, based on the positive feedback and latch-up phenomena, a steep subthreshold swing of approximately 1 mV/dec and a high ON/OFF current ratio of the order of 1010 were achieved. Building on these characteristics, we successfully verified NAND LIM operation in the p-channel mode and NOR LIM operation in the n-channel mode by connecting two of the proposed devices in parallel. The reconfigurable silicon transistor proposed in this study could perform both NAND and NOR LIM operations while sharing the same device structure and can be expected to play a key role in implementing high-density, low-power LIM systems in the future.