Cadence Design Systems (India)

This paper presents a graphical approach to design a distributed series matching network (DSMN). The proposed approach extends the conventional Smith Chart-based matching techniques that target a single spot frequency. DSMN iteratively transforms frequency-dependent complex impedances across the entire target band through reactance cancellation at band edges and impedance rotation, visually guided by the Smith Chart. This approach is validated by the design of a 6 W broadband power amplifier (PA) using CGH40006P GaN HEMT operating across 2.1-2.7 GHz (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 5 \%}$</tex> fractional bandwidth). The PA achieves more than 60% power-added efficiency (PAE), 15.3-18.9 dB gain, 38.5 dBm output power, and input/output return losses better than 5 dB across the 600 MHz bandwidth. The design methodology establishes a new paradigm for broadband matching, well suited for modern wireless communication systems demanding consistent performance over wide frequency ranges.

This work proposes two third-order bandpass filters (BPFs): one with low-side transmission zero and the other with high-side transmission zero, to improve the selectivity of the passband. To generate the transmission zero, the structure relies primarily on the electrical coupling of two folded open-loop resonators that are internally coupled with a half-wavelength resonator. A half-wavelength resonator exhibits high-side transmission zero of the passband upon symmetric coupling and low-side transmission zero upon antisymmetric coupling. The proposed filters are designed using coupling matrix synthesis, considering the necessary parameters. Two example filters are designed and fabricated at a center frequency of 1.9 GHz, with a bandwidth of 230 MHz and a return loss of more than 20 dB.

Power-aware tests are increasingly critical for both functional operation and manufacturing because of the challenges posed by high power dissipation in various test modes. Excessive test power can lead to multiple issues during manufacturing and field tests, particularly during scan-shift operations, which exhibit high switching activity and potentially excessive IR drop. Reducing IR drop during testing is essential to prevent delay-induced yield loss. To address this, we propose a Design for Testability (DFT)-based approach that controls the number of chains loading scan data at any given time using DFT-inserted registers. The Automation Test Pattern Generation (ATPG) tool automatically configures these registers, generating appropriate control bit values for the test patterns. This approach applies to both stuck-at-fault and dynamic (at-speed) test sets. Identifying the right switching control is challenging, as it must balance coverage and pattern counts. Design-dependent heuristics used for scan switching values affect the Quality of Results (QoR) achieved by the ATPG engine. This paper presents an evaluation framework for structural testing that leverages a Machine Learning-driven (ML) flow to identify switching activity and achieve optimal QoR. With the approach followed, we observed a decreasing trend in the number of instances for which IR drop is more than the threshold of 20% during scan shift. The improvement seen can be presented as the number of instances for which IR drop during the scan shift is violating the threshold of 20% reducing from~1 million instances to 42 and 72 instances on two experimental designs with relatively no impact on the area.

Failure analysis at PCB level has its own challenges. There is complexity of setting up simulation for system-level exploration. The paper will propose methods to address the challenges. It proposes three-step methodology for fault analysis. Firstly, automatically break the PCB design into simulate-able circuits. This will require depth-first traversal of the design. The second step will be coarse-stimulus detection. Coarse stimulus detection technique will intelligently look-up repository of circuits maintained in a knowledge-base and find a circuit from repository that best matches the circuit-under-test. Functionality of circuit, class of stimulus stored, input and output nodes of the circuit matched from the repository, will be applied to circuit-under-test. Paper will discuss technique adopted for storing circuit information. It will also discuss technique for finding the best-circuit match using subgraph isomorphism. Once the class of stimuli is identified – sine, dc or pulse, next step is to find parameters for the stimuli. The third step is fine stimulus detection. Fine stimulus detection method will focus on finding parameters for stimulus, such as frequency, voltage for fault detection. It will use techniques such as Monte Carlo analysis and Bode analysis to ensure maximum fault coverage. With techniques proposed, challenge of simulation setup of system for fault analysis and functional safety will be greatly addressed, improving quality and confidence in results.

This work proposes and investigates the performance of a feedback field effect device for biosensing applications. The device features a partially gated structure, enabling its function as a high-sensitivity, charge-modulated biosensor. The sensing principle relies on the regenerative feedback effect, giving rise to ultra-steep slope characteristics. 2D TCAD device simulations show a sharp turn-on profile in response to the conjugation of charged biomolecules on the ungated sensing surface. Consequently, a large shift in turn-ON voltage (AV<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</inf>) exceeding 50% and a very high current sensitivity (~10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup>) are observed, which are both considerably higher than the conventional FET-based biosensors. By simultaneously achieving a high ON-state current, low leakage, and ultra-steep subthreshold slope at low voltages, the device overcomes limitations of the existing steep slope FET biosensors. Given these attributes and compatibility with the standard semiconductor process technology, the proposed device is a promising candidate for future ultra-high sensitivity integrated biosensors.

New technologies are being used in industry automation, like service, and communications. Industry 4.0 is the next fourth industrial revolution that promotes organisational effectiveness. Automation is an exponentially growing field with various challenges. Robotic arms also known as articulated robotic arms, are widely used in the process of automation in industry 4.0. The main function of automation using robotic arms is to perform a given task repeatedly, which is greatly reducing the manual efforts and increasing the efficiency of the task performed. This paper consists of the development of a three-axis robotic arm for industry 4.0, which performs the instantly learned task repeatedly. This is designed using a servo system for more accuracy and an analog system to control the servo system using an Arduino UNO microcontroller. The designed 3-axis robotic arm performs various operations and basic services which may be difficult for human being to do so and shows accurate and fast performance in industry operations to enhance more productivity.

Hierarchical Test (HT) is a partitioned-test method designed to improve performance and decrease resources costs in both DFT and test. Specifically, this methodology eases computing resources and run times required for automatic test pattern generation. It also presents unique challenges and opportunities for diagnosis. Physically aware volume diagnostics greatly improves callout resolution, enabling diagnosis of new fault types, but presents scalability and deployability issues for hierarchical core-based designs. This paper highlights solutions needed to create an efficient high-volume physical diagnosis pipeline for hierarchical core-based designs. Results of the proposed system are demonstrated on 7nm processor designs.

Consider a set S of stores that procure goods, and another set W of warehouses that stock the goods and supply them to these stores. Each warehouse has some associated establishment costs, and different transportation costs for transporting goods to each of the stores. This is the basic structure of the Warehouse Location Problem (WLP), which aims to satisfy all the store demands for goods by opening (a subset of) warehouses and routing each store demand to a specific warehouse while optimizing some parameter, such as the total outlay, profit and/or efficiency. WLPs and their variants are well studied in optimization and find application in various scenarios, such as the effective planning of healthcare facilities, location of transportation terminals, placement of power plants and waste disposal site selection. In this chapter, a variant of the WLP problem – called the 'Capacitated facility location problem with store incompatibilities'– is studied. In this variant there are additional constraints that prevent some pairs of stores from being supplied by the same warehouse. The objective of the problem is to minimize the total cost incurred in opening warehouses and allocating warehouses to stores for the supply of goods, while ensuring that there are no conflicting allocations to the same warehouse. Two different heuristic strategies for solving the problem – one based on an effective approximation method for transportation problems and another that uses a greedy approach – are explored and compared. The performance of the two approaches is systematically evaluated on a set of standard benchmark problems.

Abstract An ongoing pandemic SS-RNA viral infection initiated from the Chinese province has threatened people throughout the globe. Coronavirus or COVID-19 or 2019-nCoV as a contagious infection is spreading day-by-day threatening the livelihood of people. The main objective of this paper is to find out solutions for the detection of this contagious viral infection at the earliest. Computer-based artificial intelligence can be used to monitor and detect the symptoms of coronavirus. For detection of coronavirus infection, computers or smartphones can be embedded with biosensors that will perceive the information and will convert the information into digital data. In this paper, a study on the coronavirus is done and an IoT-based framework is proposed to detect the coronavirus using IoT-based sensors. The proposed approach will be able to detect the pandemic in its early stages, and better options for prevention and cure will be discussed.KeywordsCOVID-19ContagiousArtificial intelligenceBiosensorIoTX-ray