Embedded Test Technology - Brief History, Current Status, and Future Directions

Area-efficient high-coverage LBIST

Summary form only given: Embedded memories have become the fastest growing segment of Systems on Chip (SoC) in recent years. According to the International Technology Roadmap for Semiconductors, embedded memories will continue to dominate the increasing SoC chip area in the future, approaching 94% within one decade. Hence, these memories will severely impact all aspects of SoC manufacturing including yield, quality and reliability. Additionally, nanotechnology is causing higher levels of device-parameter variations and new failure mechanisms that are not yet well understood. Precise fault modeling to design efficient tests is therefore essential in order to keep the test cost and test time within economically acceptable limits, while keeping higher product quality. The objective of this embedded tutorial is to provide test engineers, memory and ASIC designers, researchers, students and managers with an overview of state-of-the art of fault models for SRAMs (including traditional fault modeling and recent development in fault models for current and future technologies), together with the latest test algorithms. Next, it describes test plans, which are shown to depend on the size of the SRAM, its speed, and the application domain. Thereafter, industrial results are used to show the effectiveness of the proposed algorithms and stresses. Last, future challenges in embedded memory testing (e.g., in fault modeling, test design) are highlighted.

Scan-based logic built-in self-test (LBIST) is widely used for supporting the in-system test in automotive systems. Although this technology has the advantage of low-cost testing, it suffers from low fault coverage and high switching activity during the test. This can lead to many undetected defects, excessive heat dissipation, and IR drop, inducing catastrophic risks to functional safety. Therefore, improving these two key factors is crucial to alleviate reliability problems in the automotive domain. Most previous works have focused on controlling the enormous toggling level of random patterns; however, one of the main disadvantages of these approaches is low fault coverage. Unfortunately, additional hardware costs associated with memory elements or test points are required for detecting the remaining faults. We propose a novel LBIST scheme based on weight-aware scan grouping and scheduling (WGS) to overcome these difficulties. Since the required test time of each automotive product is limited, the proposed scheme freezes the test time and focuses on improving both aforementioned factors significantly. Our approach divides scan cells into two categories: the coverage-efficient scan group and power-efficient scan group, and then it conducts weight-based scan cell reordering. Biased random patterns are fed to enhance fault coverage for the first category. For the second category, scheduling and disabling are performed to reduce switching activity. Finally, physical-aware reordering based on an inverter is performed to reduce routing overhead. Experimental results demonstrate the feasibility of the WGS methodology on the ITC’99 and OpenRISC benchmark circuits.

Integrated circuits used in critical and high reliability applications have often strict test requirements including high test coverage and limited test time. Achieving a high test coverage using built-in self-test (BIST) has proven difficult. Methods such as test point insertion or deterministic BIST can provide high test coverage but introduce significant area overhead and design effort. In this paper, we propose a computational algorithm that uses a linear XOR model of the logic BIST (LBIST) structure and fault partitioning to extract seeds for partial ATPG patterns. Partial ATPG patterns are used to decrease the complexity of the algorithm when solving linear XOR equations to generate deterministic seeds. The extracted seeds are stored in a nonvolatile memory on- or off-chip. Results show that for most designs, patterns generated from the extracted ATPG seeds are significantly more effective in detecting faults and can achieve higher test coverage than LBIST.

A comprehensive review: Fault detection, diagnostics, prognostics, and fault modeling in HVAC systems

This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment in the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.

Plant height plays an important role in crop yield, product quality, and cultivation management. However, the physiological mechanisms that regulate the establishment of plant height in alfalfa plants remain unclear. Herein, we measured plant height traits, leaf characteristics, photosynthetic physiology, cell wall composition, and endogenous hormone contents of tall- and short-stalked alfalfa materials at different reproductive periods. We analyzed the physiology responsible for differences in plant height. The results demonstrated that the number of internodes in tall- and short-stalked alfalfa materials tended to converge with the advancement of the fertility period. Meanwhile, the average internode length (IL) of tall-stalked materials was significantly higher than that of short-stalked materials at different fertility periods, with internode length identified as the main trait determining the differences in alfalfa plant height. Leaf characteristics, which are closely related to photosynthetic capacity, are crucial energy sources supporting the expression of plant height traits, and we found that an increase in the number of leaves contributed to a proportional increase in plant height. Additionally, a significant positive correlation was observed between plant height and leaf dry weight per plant during the branching and early flowering stages of alfalfa. The leaves of alfalfa affect plant height through photosynthesis, with the budding stage identified as the key period for efficient light energy utilization. Plant height at the budding stage showed a significant positive correlation with soluble sugar (SS) content and a significant negative correlation with intercellular CO2 concentration. Moreover, we found that alfalfa plant height was significantly correlated with the contents of indole-3-acetic acid in stem tips (SIAA), gibberellin A3 in leaves (LGA3), zeatin in stem tips (SZT), and abscisic acid in leaves (LABA). Further investigation revealed that SS, SIAA, and LGA3 contents were important physiological indicators affecting alfalfa plant height. This study provides a theoretical basis for understanding the formation of alfalfa plant height traits and for genetic improvement studies.

This study evaluates how implementing the ISO 9001:2015 Quality Management System affects marketing performance and product quality at PT Timah Tbk. The company faced production fluctuations in 2023, leading to a sharp revenue decline and significant losses. Using a quantitative descriptive approach with a survey method, the research analyzes data from employees and management. The findings show that ISO 9001:2015 implementation positively impacts product quality (path coefficient = 0.9867) and quality management (path coefficient = 0.9036). The R-Square value indicates that ISO 9001:2015 explains 81.64% of product quality improvements, while product quality accounts for 97.38% of marketing performance changes. These results suggest that effective quality management through ISO 9001:2015 enhances marketing efficiency and strengthens customer trust, helping the company navigate competition in the tin industry.

Product quality in the tofu industry has been a concern in various studies, but research that specifically discusses strategies for improving product quality in traditional tofu businesses in relation to increasing the number of customers remains limited. This study aims to analyze product quality improvement strategies in increasing the number of customers at Usaha Tahu Asri in Bonjol Subdistrict, Pasaman Regency. This study employed a qualitative approach with a case study design, involving informants consisting of the business owner, workers, and consumers selected through purposive sampling. Data were collected through in-depth interviews, direct observation, and documentation, and were then analyzed using qualitative data analysis techniques through the stages of data reduction, data presentation, and conclusion drawing. The results showed that product quality was not yet consistent, which was influenced by the use of non-standardized raw materials, production processes that were still manual without standard operating procedures, and quality control that was not yet systematic. In addition, limited product innovation and the low skill level of workers also affected product quality and the number of customers. These findings contribute to the development of studies on product quality and strategic management in the context of micro-enterprises based on traditional production, as well as broadening understanding of the importance of product quality in increasing the number of customers. The conclusion of this study affirms the importance of standardizing raw materials, production processes, quality control, and improving workers’ skills to enhance product quality and support business sustainability. The implications of this study include a theoretical contribution to the development of the MSME literature as well as practical implications for business actors in improving competitiveness.

There has been an increasing drive to improve total quality management of manufactured products internationally in recent years. This has resulted in many improvements in the quality and management of quality in New Zealand firms. Research was carried out in New Zealand to compare the opinions of manufacturing company managers about product quality with general public opinions. Probes perceptions of recent changes in product quality and compares the quality of New Zealand made goods with the quality of those made overseas. Most managers and consumers agreed that the quality of New Zealand made goods is now better than most overseas made goods. Shows a great improvement in the public perception of New Zealand made goods in recent years. Managers are still much stronger than consumers in their belief in local made high quality, which may have resulted in some complacency regarding management of product quality.

Meeting the quality and reliability requirements of the ISO 26262 and other automotive electronics standards will only become more difficult as device sizes and complexities continue to grow. New advanced test technologies such as cell-aware ATPG, hybrid compression / logic BIST / memory BIST and diagnosis-driven yield analysis with RCD provide some key building blocks towards ensuring compliance to the new standards. Adoption of these and other advanced test capabilities will not only improve the ability of semiconductor manufacturers to achieve necessary quality and reliability metrics, but will also help to further differentiate their products by delivering embedded test capabilities that can be leveraged by their customers at the system level and in the field.

In view of the diversity, complexity and magnanimity of information data in the operation status of new energy power plant, the data mining technology is applied to fault diagnostics. On this basis, the overall process of fault diagnostics methods including clustering analysis, fault rule mining, fault modeling and other mathematical statistical theories is designed. The logical relationship between massive data is redefined at a deeper level. And the most effective fault characterization is extracted. The correctness and validity of the method are proved by comparing the curve of the fault characteristic parameter prediction results with the field measurement results. This method has high data processing efficiency, effectively improves the reliability and accuracy of fault diagnostics and early warning. And it provides a strong guarantee for the safe operation of the new energy grid-connected.

The article discusses an approach to choosing a production modernization strategy based on product quality management. A description of possible modernization directions in which the enterprise can improve production parameters is provided. The result of modernization is an improvement in product quality for certain costs, which allows the enterprise to adjust the price of the products. To find the best price-quality balance, we propose an approach to assessing modernization strategies based on production parameters. A variety of modernization strategies are modeled using a stochastic method which generates random parameters within specified ranges (Monte Carlo method). Product quality is assessed using a multiparameter criterion. The parameters of machine-building production are modeled using the example of bearings with different numbers of iterations. To validate the calculation, the calculated values were compared with the values obtained by iterating through all possible production modernization strategies. The necessary number of iterations was determined, at which the comparison showed a deviation of values not exceeding 5 %, confirming the applicability of the approach for industrial enterprises. Recommendations on the application of the proposed approach to choose a strategy for the modernization of mechanical engineering production are provided.

This paper presents a low-cost production test strategy for digitally-calibrated analog-to-digital converters (ADCs) that incorporate an equalization-based background calibration scheme. The test time of these designs is dominated by the long calibration time required prior to conducting the final testing. To reduce overall test time, we present a two-step calibration approach that significantly reduces calibration time without compromising test coverage. In addition, by analyzing the data obtained in calibration, devices that fail certain static or dynamic specifications can be identified without incurring any additional test time beyond calibration, thereby enabling early rejection. To minimize calibration time and maximize failing symptoms for fault detection, we propose using specific calibration stimuli. Simulation results for a pipelined ADC shows that the proposed strategy reduces the total test time by 80%. This is achieved by reducing the calibration time, as well as by prescreening a good fraction of defective devices that fail static and dynamic specifications including the gain/offset errors and the effective number of bits (ENOB).

Diesel engines are widely used due to their high reliability, high thermal efficiency, fuel availability, and low consumption. They are used to generate power, e.g., in passenger cars, ships, power plants, marine offshore platforms, and mining and construction machines. The engine is at heart of these applications, so keeping it in good working condition is vital. Recent technical and computational advances and environmental legislation have stimulated the development of more efficient and robust techniques for the diagnostics of diesel engines. The emphasis is on the diagnostics of faults under development and the causes of engine failure or reduced efficiency. Diesel engine fuel injection plays an important role in the development of the combustion in the engine cylinder. Arguably, the most influential component of the diesel engine is the fuel injection equipment; even minor faults can cause a major loss of efficiency of the combustion and an increase in engine emissions and noise. With increased sophistication (e.g., higher injection pressures) being required to meet continuously improving noise, exhaust smoke, and gaseous emission regulations, fuel injection equipment is becoming even more susceptible to failure. The injection systems have been shown to be the largest contributing factor in diesel engine failures. Extracting the health information of components in the fuel injection system is a very demanding task. Besides the very time-consuming nature of experimental investigations, direct measurements are also limited to selected observation points. Diesel engine faults normally do not occur in a short timeframe. The modeling of typical engine faults, particularly combustion related faults, in a controlled manner is thus vital for the development of diesel engine diagnostics and fault detection. Simulation models based on physical grounds can enlarge the number of studied variables and also obtain a better understanding of localized phenomena that affect the overall behavior of the system. This paper presents a survey of the analysis, modeling, and diagnostics of diesel fuel injection systems. Typical diesel fuel injection systems and their common faults are presented. The most relevant state of the art research articles on analysis and modeling of fluid injection systems as well as diagnostics techniques and measured signals describing the behavior of the system are reviewed and the results and findings are discussed. The increasing demand and effect of legislation related to diagnostics, especially on-board diagnostics (OBD), are discussed with reference to the future progress of this field.