Automatic Test Equipment Research Articles

Logic Diagnosis Based on Logic Built-In Self-Test Signatures Collected In-Field from Failing System-on-Chips

Modern embedded nanoelectronic devices, particularly in safety-critical sectors, require high dependability throughout their lifecycle. To address this, designers have started integrating extra circuitry for on-device self-testing, such as the Logic Built-In Self-Test (LBIST). However, while automatic test equipment (ATE) ensures exhaustive testing during manufacturing, in-field testing capabilities are limited. This study introduces a novel methodology for in-field data collection of failure information from LBIST engines and a subsequent logic diagnosis strategy to facilitate failure analysis of field returns. The information is collected from key-on and key-off self-tests, executed by central processing units (CPUs) with a fixed seed and different frequency configurations, primarily addressing transition delay (TRN) faults. The proposed approach capitalizes on the constrained in-field configurability of LBIST and does not require a custom architecture, making it highly practical and readily applicable to real-world devices. The logic diagnosis strategy significantly reduces the number of candidate faults by exploiting the first failing pattern index found during the in-field testing and data collection. Reducing fault candidates could enhance accuracy during failure analysis, especially when field return devices exhibit a “No Trouble Found” (NTF) behavior. The experimental results are reported for ITC’99 benchmarks and an industrial automotive system-on-chip (SoC) produced by STMicroelectronics, with about 20 million gates.

A Modified and Customized Wingsuit Flying Search (MCWFS)-based Approach for the Circular Scan Architecture

The circular scan architecture (CSA) is a widely used scan design technique in digital circuit testing that involves scanning test data through a circuit in a circular pattern. Several optimization approaches have been suggested so far to lessen the Test Data Volume (TDV), but no one has shown satisfactory results. Therefore, this paper proposes a Modified and Customized Wingsuit Flying Search algorithm to decrease the test data volume time. The proposed MCWFS-SBA-CSA introduces some novel features that distinguish it from traditional circular scan approaches. The concept of wingsuit flying is an innovative approach in the field of scan systems. This allows the scan that moves more dynamically and efficiently, enabling it to cover a larger area in a shorter time than traditional circular-scan approaches. After this phase, only scan-chosen inputs are required to obtain the next test pattern from the captured response. Therefore, required less Automatic test equipment (ATE) memory in this research so there is a Decreased cost in system-on-chip (SOC). The proposed method attains fast global searching capacity. The efficiency of the proposed technique attains 26.42%, 32.45%, and 29.34% higher compression ratio (CR), Test Application Time (TAT) reduction of SC-128 attains 29.45%, 34.67%, and 40.22% higher, TDV of SC-256 attains 32.23%, 36.34%, and 39.23% higher than existing methods, such as TDV for Circular Scan Approaches utilizing Modified Shuffled Shepard Optimization (TDV-CSA-MSSO), Effectual Very-large-scale integration Test data compression system for CSA under modified and colony metaheuristic (ETDC-CSA-MACMH), and Test Data Compression Utilizing Tetrad state Skip System (TDC-DC-TSSC) for Digital Circuits, respectively.

Metagenomic Analysis during Co-Digestion Buffalo Sludge and Tomato Pomace Post Thermal Stress: A Case Study

The tomato industry and buffalo farming generate waste, including sludge (BS) and tomato pomace (TP), which can significantly impact their economic and environmental sustainability. The case study tracked changes in microflora composition after a thermal shock during anaerobic co-digestion. The inoculum-to-substrate ratio was 0.5 based on volatile solid content under mesophilic conditions. An Automatic Methane Potential Test System was used to monitor the process before and after thermal stress (50°C) occurred for three days. Next-generation sequencing analyzed the bacterial and archaeal communities. The pH decreased, and methane production plateaued due to the high volatile solid content (87 g/L). After thermal stress, the pH returned to neutral, and the batch resumed biogas production. The cumulative CH4 production reached 3,115 Nml. The biogas had a maximum methane peak of 78.5% compared to 58.4% in BS. The taxonomic classification showed that Firmicutes (51.7%) and Bacteroidetes (29.9%) represented 81.6% of the total OTUs among the bacteria. Fonticella, the most abundant Clostridiaceae (average 4.3%), was absent in BS and increased (up to 17.1%) in TP during methane production. Methanocorpusculum was the most abundant in the archaeal community. However, Metanosarcina showed a stronger correlation with methane production. Brief thermal stress significantly altered bacterial and archaeal populations and allowed to resume biogas production.

Development of ECG Common Mode Rejection Ratio Automatic Test System

To comprehensively meet the test requirements for the common mode rejection ratio (CMRR) across different ECG-particular standards, this paper presents the design of an ECG CMRR automatic test system. The hardware component primarily consists of a test signal generation module, an automatic control network (which includes a resistance-capacitance network control module and a polarization voltage control module), and a noise level switching module. The software portion enables automatic control and user interaction. Experimental results indicate that the system is stable, reliable, and highly automated, capable of satisfying the test requirements of various ECG-particular standards, thus demonstrating a promising application prospect.

RESEARCH ON ARTIFICIAL INTELLIGENCE TOOLS FOR AUTOMATING THE SOFTWARE TESTING PROCESS

The subject matter of the article is artificial intelligence (AI) tools for automating the software testing process. The rapid development of the software development industry in recent decades has led to a significant increase in competition in the IT technology market and, as a result, stricter requirements for corresponding products and services. AI-driven test automation is becoming increasingly relevant due to its ability to solve complex tasks that previously required significant human resources. The goal of the work is to investigate the possibilities of using AI technologies to automate manual testing processes, which will increase testing efficiency, reduce costs, and improve software quality. The following tasks were solved in the article: analysis of existing tools and approaches to test automation using AI; development of a conceptual model of a system that integrates AI into the testing process; exploring the potential of AI to automate various aspects of software testing, such as generating test scenarios, detecting defects, predicting errors, and automatically analyzing test results. The following methods are used: theoretical analysis of the literature and existing solutions in the field of test automation, experimental study of the effectiveness of the proposed test automation methods. The following results were obtained: the concept of a system that integrates AI technologies for automating software testing is presented. It has been found that the use of AI allows automating routine testing tasks, significantly reducing the number of human errors, and improving the quality of software products and the effectiveness of verification and validation processes. Conclusions: The development and implementation of AI-based testing automation systems are extremely relevant and promising. The use of AI technologies makes it possible to significantly increase the efficiency of testing, reduce the costs of its implementation, and improve the quality of software. The proposed approach to the development of an AI-based test automation system can be used as a basis for further research and development in this field.

Design and Realization of Fully Automatic Pump Performance Test System

With the development of automation technology, it gradually replaces manual labor in various fields. Pumps are widely used in various production processes in the industrial field. To enhance the automation level of the pump production and testing process will significantly reduce production costs. This paper aims to design and realize a set of fully automatic, high-precision pump performance testing system. The fully automated operation of the performance test is designed according to the flow rate automatic sampling method, to achieve the accurate measurement of the pump operating conditions. Utilizing the PID algorithm, the valve is automatically adjusted through PLC control to realize the accurate positioning of the flow measurement point. With the help of the software program to carry out the calculation and analysis of test data, to achieve the visualization of the test process of the whole performance test data management. At the same time, SQL Server database is used to realize rapid query and management of test data. After the test verification, the system realizes the full-automatic measurement of the pump performance curve, and the measurement accuracy conforms to the standard of ISO 9906:2012.

Advances in Traditional Electrical Validation of High-Speed Serial Connections Using Innovative Debugging Methods and Machine Learning Models.

As technology continues to shrink and more heterogeneous blocks are combined into single System on a chip (SOCs), the design of high-performance integrated circuits is getting more and more difficult. In the course of silicon validation, which includes testing (Wafer Testing, Automatic Test Equipment), digital validation (Functional Data route, FSM), analogue bench validation (Electrical, Interoperability), and system validation, a number of challenges that arise from the complicated design are uncovered. The process, methods, and constraints of each domain are unique. Electrical problems that have already passed through earlier stages of design are the primary focus of analogue validation. The analogue signal or mix signal nature of IPs makes it impossible for digital validation or any of the other post-silicon domains to discover electrical flaws on its own. As a result, studies focusing on analogue bench validation have grown in prominence. Recent developments in analogue bench validation, focusing on high-speed serial interface in particular, are highlighted in the proposed study. In addition, this article summarises recent technological developments that provide avenues for further study regarding analogue bench validation.

FATE: A Flexible FPGA-Based Automatic Test Equipment for Digital ICs

The limits of chip technology are constantly being pushed with the continuous development of integrated circuit manufacturing processes and equipment. Currently, chips contain several billion, and even tens of billions, of transistors, making chip testing increasingly challenging. The verification of very large-scale integrated circuits (VLSI) requires testing on specialized automatic test equipment (ATE), but their cost and size significantly limit their applicability. The current FPGA-based ATE is limited in its scalability and support for few test channels and short test vector lengths. As a result, it is only suitable for testing specific chips in small-scale circuits and cannot be used to test VLSI. This paper proposes a low-cost hardware and software solution for testing digital integrated circuits based on design for testability (DFT) on chips, which enables the functional and performance test of the chip. The solution proposed can effectively use the resources within the FPGA to provide additional test channels. Furthermore, the round-robin data transmission mode can also support test vectors of any length and it can satisfy different types of chip test projects through the dynamic configuration of each test channel. The experiment successfully tested a digital signal processor (DSP) chip with 72 scan test pins (theoretically supporting 160 test pins). Compared to our previous work, the work in this paper increases the number of test channels by four times while reducing resource utilization per channel by 37.5%, demonstrating good scalability and versatility.

Evaluating the Cost-Efficiency and Financial Sustainability of Cloud-Based ATE Solutions in Testing Environments

The introduction of cloud-based solutions in testing environments, especially in Automated Test Equipment (ATE), is bringing a big change in the way organizations approach testing processes. This change is driven by the potential for enhanced cost-efficiency and financial sustainability. As testing requirements continue to evolve, the scalability, flexibility, and accessibility offered by cloud-based ATE solutions present promising opportunities for optimizing resource utilization and reducing operational costs. This study aims to evaluate the economic feasibility of adopting cloud-based ATE solutions. It explores their impact on both short-term and long-term financial sustainability. By examining factors such as upfront investment, operational expenses, and the overall return on investment, this research seeks to provide insights into the practical implications of transitioning to cloud-based ATE solutions for organizations seeking to streamline their testing processes.

External quality assessments for SARS-CoV-2 genome detection in Austria : Acomparison of the first postpandemic round with results from the pandemic era.

External quality assessment (EQA) schemes provide objective feedback to participating laboratories about the performance of their analytical systems and information about overall regional analytical performance. The EQAs are particularly important during pandemics as they also assess the reliability of individual test results and show opportunities to improve test strategies. With the end of the COVID-19 pandemic, the testing frequency significantly decreased in Austria. Here, we analyzed whether this decrease had an effect on participation and/or performance in SARS-CoV‑2 virus detection EQAs, as compared to the pandemic era. Identical samples were sent to all participating laboratories, and the EQA provider evaluated the agreement of the reported results with defined targets. The EQA was operated under two schemes with identical samples and therefore we analyzed it as asingle EQA round. The performance of testing was reported as true positive ratios, comparing the post-pandemic data to previous rounds. Furthermore, subgroups of participants were analyzed stratified by laboratory type (medical or nonmedical) and the test system format (fully automated or requiring manual steps). While the frequency of false negative results per sample did not change during the 3 years of the pandemic (5.7%, 95% confidence interval [CI] 3.1-8.4%), an average per sample false negative ratio of 4.3% was observed in the first post-pandemic EQA (0%, 1.8%, and 11% for the 3 positive samples included in the test panel, n = 109 test results per sample). In this first post-pandemic EQA medical laboratories (average 0.4% false negative across 3samples, n = 90) and automated test systems (average 1.2% false negative, n = 261) had lower false negative ratios than nonmedical laboratories (22.8%, n = 19) and manual test systems (16.7%, n = 22). These lower average ratios were due to alow concentration sample, where nonmedical laboratories reported 36.8% and manual test systems 54.5% true positive results. Overall ratios of true positive results were below the mean of all results during the pandemic but were similar to the first round of the pandemic. Alower post-pandemic true positive ratio was associated with specific laboratory types and assay formats, particularly for samples with low concentration. The EQAs will continue to monitor the laboratory performance to ensure the same quality of epidemiological data after the pandemic, even if vigilance has decreased.

Geology, geochronology, and genesis of Au mineralization from the Huangshui'an Mo-Au-Pb deposit in Eastern Qinling, China

Huangshui'an deposit, situated in the Xiong'ershan area of Qinling orogenic belt (QOB) in China, is a globally rare carbonatite-type molybdenum polymetallic deposit that contains economic Mo, Au, Pb, and REE mineralization. The molybdenite Re-Os ages (Ca. 213.5–209.2 Ma) indicates that Huangshui'an Mo mineralization formed at Late Triassic. However, it is still unclear whether the Au mineralization is related to carbonatite dykes, and the formation age and genesis mechanism of Au mineralization remain poorly understood at Huangshui'an. This paper presents a comprehensive investigation of the Au mineralization events at the Huangshui'an deposit, including geology, mineralogy, and geochronology. The aim is to provide insights into its formation and mineralization processes and offer a broader understanding of regional Au metallogeny. Based on petrographic investigations, four stages are identified in the Au mineralization as follows: (I) quartz-K-feldspar stage; (II) quartz-pyrite-gold stage, including (IIA) pyrite-native gold sub-stage and (IIB) gold-tellurite-bismuthide sub-stage; (III) quartz-pyrite-polymetallic sulphide-oxide stage; and (IV) calcite-quartz-chlorite stage. The LA-ICP-MS titanite U-Pb dating yields a weighted mean age of 130.8 ± 0.63 Ma which determined that the Au mineralization at Huangshui’an was in the Early Cretaceous and was an independent mineralization event, which may be related to the lithosphere's destructive effect on the North China Craton (NCC). Combined with mineralography by automatic quantitative mineral analysis and testing system (TIMA), we consider that tellurium-bismuth minerals are closely associated with Au mineralization. The sulfur and tellurium fugacity of the ore-forming fluids in each stage were calculated using metal sulfide-oxides and tellurium-bismuth minerals. It was concluded that the precipitation of Au in the Huangshui'an deposit was mainly due to the combined effects of sulfidation, gold collection-redissolution by the low-melting point chalcophile elements (LMCE) melts, and the variation of sulfur fugacity and tellurium fugacity.

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Traditional manual testing methods are no longer able to meet the maintenance needs of modern shipboard fire control system board-level circuits. Therefore, the development of intelligent and universal automatic test systems has become an important issue for shipboard fire control system fault detection. This article analyzes the performance and functional requirements of the automatic test system, develops the automatic test system based on PXI bus instruments, and designs an overall solution with signal time-frequency analysis capabilities that can carry out intelligent fault diagnosis for a variety of board-level circuits. On this basis, the design of hardware circuit and software solution is completed. The automatic test system was analyzed on hardware indicators and software functions. On the hardware side, the hardware resource selection was completed based on the hardware indicator requirements. On the software side, the overall software architecture design and the design of each functional module were completed based on the system software functional requirements. Finally, the composition and functions of each module of the system software and the overall operation process are introduced.

Design of Fault Diagnosis Method Based on Circuit Frequency Domain Characteristics Fault Dictionary Method

In recent years, with the rapid development of integrated circuits and the widespread application of smart chips, the internal structure of complex electronic systems have become increasingly complex, which greatly increases the difficulty of board-level circuit testing and fault diagnosis of complex electronic systems. At present, traditional manual testing methods are no longer able to meet the maintenance requirements of modern complex electronic system board-level circuit. Therefore, the development of intelligent and universal automatic testing systems has become an important issue in fault detection of complex electronic systems. Based on the testing requirements of the automatic test system(ATS), this paper designs the corresponding overall scheme, software scheme and hardware circuit, and studies the fault diagnosis and test of board-level circuit of complex electronic system, and proposes to apply the traditional Bode diagram to the engineering practice of ATS. Fault detection is realized by utilizing the frequency domain characteristics of the object under test.

Test and characterization of SiPMs for the upgrade of MEGII high resolution Timing Counter

The MEG II experiment based at PSI (Zuerich, Switzerland) has been committed and is taking data since 2021 to improve sensitivity on the decay μ+ → e+ γ. The pixelated Timing Counter (pTC), consisting of two arrays of 512 5 mm thick scintillator pixel each, read out by 6 3×3 mm2, 50 μm cell, Silicon Photomultipliers (SiPMs) from AdvanSiD, for a total of 6144 SiPM, achieves an overall resolution in the positron impact time of ∼43 ps when exploiting multiple measurements. To additionally improve this resolution, 4×4 mm2, 40 μm cell SiPMs have been selected to substitute a fraction of the old ones (about 1000 of them overall). By means of an automated test system, a first group of them has been characterized (measuring their breakdown voltage and their I-V curves), to match as much as possible SiPMs with the same gains in each pixel, in order to maximize the pixel time resolution. Such automated test system will be presented, together with some preliminary results on single pixel time resolution and the expectations of the average time resolution of the pTC in the coming years.

CIVA Modelling Module for Zonal Discrimination Method Part 2-System Qualification Aspects

Automatic Ultrasonic Testing (AUT) systems for pipeline girth weld inspections are usually subjected to a qualification process. This process consists of assessment of performance capabilities including detection, sizing, temperature range and repeatability. One of the most demanding qualification programmes is described in DNV ST-F101 Appendix E [1]. With the introduction of the AUT module in the 2023 edition of CIVA simulation software, the incorporation of metamodeling allows users to predict detection reliability via probability of detection (POD) and sizing accuracy of the zonal discrimination method (ZDM) using amplitude apportioning. Part 1 of this project demonstrated the ability of CIVA to simulate the calibration block and strip chart output. In Part 2 we illustrate the techniques that can be used to predict POD and sizing accuracy as might be done in a system qualification. Results compare well to field data.

An Improved Particle Swarm Optimization Algorithm for Automated Test Resource Allocation

With the rapid increase of software complexity, automated testing has become the mainstream of software testing. However, the current automated test tools adopt a large number of common test equipment resource. At the beginning of the test, for specific test scenarios, the testers need to plan the test equipment resources according to the requirements of the software under test. An automatic test equipment resource planning and optimization algorithm based on group optimization is proposed IPSO (Improved Particle Swarm Optimization) to solve this problem. This method maps the requirement description to the equipment description to obtain the matching matrix, and then establishes a multi-objective constrained optimization model for test equipment resource planning. The solution is obtained through improved particle swarm optimization algorithm to achieve the optimal planning of test equipment resources. Compared with traditional manual planning methods, this algorithm reduces the workload of testers, and improves the automation and efficiency of testing. Experimental results demonstrate that the optimal solutions found by the algorithm proposed in this paper is superior to those of the PSO (Particle Swarm Optimization)) and Linearly Decreasing Inertia Weight Particle Swarm Optimization (LWPSO) algorithms.

Retracted: Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

Retracted: Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

A Manipulator Pose Planning Algorithm Based on Matrix Information Geometry

In an automatic ultrasonic testing system constituted by an ultrasonic probe and a six-axis manipulator, the manipulator needs to run from a static state to the target velocity. To prevent equipment damage caused by sudden acceleration or deceleration, it is necessary to plan the position and pose of the end effector of the manipulator at each detected point. In this manuscript, an algorithm for planning the position and pose of the manipulator is proposed based on the information geometry structure of special orthogonal groups. As the linear operation of the orthogonal matrix corresponding to the manipulator pose is not closed, the manipulator pose at each detected point was calculated using the straightness of the Lie algebra of the special orthogonal group. The matrix information geometry algorithm enabled not only the manipulator to accelerate and decelerate uniformly along the detection trajectory, but also the angular acceleration of the end effector to accelerate uniformly at first, then keep a uniform velocity, and finally decelerate uniformly. The platform motion experiments with the Turin TKB070S six-axis manipulator are carried out to verify the effectiveness of the matrix information geometry algorithm for planning the pose of the manipulator.

Optimizing Brain Tumor Classification Accuracy Through Transfer Learning and Internet of Things Integration

Brain tumor classification using medical images is crucial for identification and therapy. However, brain tumors are complex and vary, making grouping them difficult. This work demonstrates a novel transfer learning method for brain tumor classification. We employ trained Convolutional Neural Networks (CNNs) models and data enrichment approaches to extract meaningful information from medical images. We want to fine-tune the models built on our dataset to uncover hierarchical patterns that distinguish tumor types. Through data enrichment, the training sample becomes more diverse and richer, making the model more generic and robust. Our team's extensive testing and research have shown that the suggested procedure can identify brain tumors. Our machine-learning approach performs better than others in terms of accuracy, sensitivity, specificity, and precision. Our technique improves brain tumor categorization and assures accurate clinical diagnosis. Automated testing systems are one way for physicians to assist patients in selecting the best course of treatment. Researchers may improve classification performance by incorporating modern imaging technology or topic-specific data. The Internet of Things, or IoT, is helping to drive the development of complex real-time data collection, processing, and sharing systems. These technological advancements have transformed medical imaging. This graphic depicts a cutting-edge transfer learning system that may be able to identify brain cancer from medical photos. This technology has the potential to enhance data collection and processing via the Internet of Things. Data augmentation and pre-trained convolutional neural networks may help to extract interpretable medical images. The Internet of Things improved the model's flexibility, resilience, and utility. We achieved this by expanding the training data set. Rapid categorization advancements have made clinical diagnosis more efficient. Classification, deep learning, medical imaging, machine learning, transfer learning, tumor detection, and image analysis all relate to this topic.

Subjective measurement of resolution of image intensifier tubes

Limiting resolution is traditionally defined as a spatial frequency of smallest resolvable element of a resolution target that can be distinguish by a human observer. This definition and measurement method have been criticized in a series of literature sources due to its subjectivity and low repeatability/accuracy. Such criticism looks reasonable as it is commonly known that results of measurement of resolution of the same tubes carried out by several test teams can differ significantly. This paper presents a detail analysis of classical subjective measurement method of limiting resolution of image intensifier tubes implemented by real test systems. The findings show that in spite of a common opinion, subjectivity of measurement is not the main reason for differences of test results carried out by different test teams. The main reasons are differences in performance of optics of test systems, use of resolution targets of different types and polarity, and inherent spatial non-uniformity of performance of image intensifier tubes. The paper also shows that due to spatial non-uniformity it is very difficult to design automatic test systems that could produce the same results as human observers using classical subjective measurement method.