Diganta Das

Bio: Diganta Das is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Reliability (statistics) & Prognostics. The author has an hindex of 22, co-authored 103 publications receiving 2262 citations.

Precursor Parameter Identification for Insulated Gate Bipolar Transistor (IGBT) Prognostics

Abstract: Precursor parameters have been identified to enable development of a prognostic approach for insulated gate bipolar transistors (IGBT). The IGBT were subjected to thermal overstress tests using a transistor test board until device latch-up. The collector-emitter current, transistor case temperature, transient and steady state gate voltages, and transient and steady state collector-emitter voltages were monitored in-situ during the test. Pre- and post-aging characterization tests were performed on the IGBT. The aged parts were observed to have shifts in capacitance-voltage (C-V) measurements as a result of trapped charge in the gate oxide. The collector-emitter ON voltage VCE(ON) showed a reduction with aging. The reduction in the VCE(ON) was found to be correlated to die attach degradation, as observed by scanning acoustic microscopy (SAM) analysis. The collector-emitter voltage, and transistor turn-off time were observed to be precursor parameters to latch-up. The monitoring of these precursor parameters will enable the development of a prognostic methodology for IGBT failure. The prognostic methodology will involve trending precursor data, and using physics of failure models for prediction of the remaining useful life of these devices.

Automated manufacturability analysis: a survey

Abstract: In the market-place of the 21st century, there is no place for traditional ‘over-the-wall’ communications between design and manufacturing. In order to ‘design it right the very first time’, designers must ensure that their products are both functional and easy to manufacture. Software tools have had some successes in reducing the barriers between design and manufacturing. Manufacturability analysis systems are emerging as one such tool — enabling identification of potential manufacturing problems during the design phase and providing suggestions to designers on how to eliminate them. In this paper, we provide a survey of current state-of-the-art automated manufacturability analysis. We present the historical context in which this area has emerged and outline characteristics to compare and classify various systems. We describe the two dominant approaches to automated manufacturability analysis and overview representative systems based on their application domain. We describe support tools that enhance the effectiveness of manufacturability analysis systems. Finally, we attempt to expose some of the existing research challenges and future directions.

Condition Monitoring for Device Reliability in Power Electronic Converters: A Review

Abstract: Condition monitoring (CM) has already been proven to be a cost effective means of enhancing reliability and improving customer service in power equipment, such as transformers and rotating electrical machinery. CM for power semiconductor devices in power electronic converters is at a more embryonic stage; however, as progress is made in understanding semiconductor device failure modes, appropriate sensor technologies, and signal processing techniques, this situation will rapidly improve. This technical review is carried out with the aim of describing the current state of the art in CM research for power electronics. Reliability models for power electronics, including dominant failure mechanisms of devices are described first. This is followed by a description of recently proposed CM techniques. The benefits and limitations of these techniques are then discussed. It is intended that this review will provide the basis for future developments in power electronics CM.

Data Center Energy Consumption Modeling: A Survey

Abstract: Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Methods and Applications

Abstract: As companies strive to develop artefacts intended for services instead of traditional sell-off, new challenges in the product development process arise to promote continuous improvement and increasing market profits. This creates a focus on product life-cycle components as companies then make life-cycle commitments, where they are responsible for the function availability during the extent of the life-cycle, i.e. functional products. One of these life-cycle components is manufacturing; therefore, companies search for new approaches of success during manufacturability evaluation already in engineering design. Efforts have been done to support early engineering design, as this phase sets constraints and opportunities for manufacturing. These efforts have turned into design for manufacturing methods and guidelines. A further step to improve the life-cycle focus during early engineering design is to reuse results and use experience from earlier projects. However, because results and experiences created during project work are often not documented for reuse, only remembered by some people, there is a need for design support. Knowledge engineering (KE) is a methodology for creating knowledge-based systems, e.g. systems that enable reuse of earlier results and make available both explicit and tacit corporate knowledge, enabling the automated generation and evaluation of new engineering design solutions during early product development. There are a variety of KE-approaches, such as knowledge-based engineering, case-based reasoning and programming, which have been used in research to develop design for manufacturing methods and applications. There are, however, opportunities for research where several approaches and their interdependencies, to create a transparent picture of how KE can be used to support engineering design, are investigated. The aim of the research presented in this thesis is to create new methods for design for manufacturing, by using several approaches of KE, and find the beneficial and less beneficial aspects of these methods in comparison to each other and earlier research. This thesis presents methods and applications for design for manufacturing using KE. KE has been employed in several ways, namely rule-based, rule-, programmingand finite element analysis (FEA)-based, and ruleand plan-based, which are tested and compared with each other. Results show that KE can be used to generate information about manufacturing in several ways. The rule-based way is suitable for supporting life-cycle commitments, as engineering design and manufacturing can be integrated with maintenance and performance predictions during early engineering design, though limited to the firing of production rules. The rule-, programmingand FEA-based way can be used to integrate computer-aided design tools and virtual manufacturing for non-linear stress and displacement analysis. This way may also bridge the gap between engineering designers and computational experts, even though this way requires a larger effort to program than the rule-based. The ruleand planbased way can enable design for manufacturing in two fashions – based on earlier manufacturing plans and based on rules. Because earlier manufacturing plans, together with programming algorithms, can handle knowledge that may be more intricate to capture as rules, as opposed to the time demanding routine work that is often automated by means of rules, several opportunities for designing for manufacturing exist.

Prognostics and health management of electronics

Abstract: There has been a growing interest in monitoring the ongoing "health" of products and systems in order to predict failures and provide warning to avoid catastrophic failure. Here, health is defined as the extent of degradation or deviation from an expected normal condition. While the application of health monitoring, also referred to as prognostics, is well established for assessment of mechanical systems, this is not the case for electronic systems. However, electronic systems are integral to the functionality of most systems today, and their reliability is often critical for system reliability. This paper presents the state-of-practice and the current state-of-research in the area of electronics prognostics and health management. Four current approaches include built-in-test (BIT), use of fuses and canary devices, monitoring and reasoning of failure precursors, and modeling accumulated damage based on measured life-cycle loads. Examples are provided for these different approaches, and the implementation challenges are discussed.

Prognostics and Health Management of Electronics

Abstract: Reliability is the ability of a product or system to perform as intended (i.e., without failure and within specified performance limits) for a specified time, in its life-cycle environment. Commonly used electronics reliability prediction methods (e.g., Mil-HDBK-217, 217-PLUS, PRISM, Telcordia, FIDES) based on handbook methods have been shown to be misleading and provide erroneous life predictions. The use of stress and damage models permits a far superior accounting of the reliability and the physics of failure (PoF); however, sufficient knowledge of the actual operating and environmental application conditions of the product is still required. This article presents a PoF-based prognostics and health management approach for effective reliability prediction. PoF is an approach that utilizes knowledge of a product's life-cycle loading and failure mechanisms to perform reliability modeling, design, and assessment. This method permits the assessment of the reliability of a system under its actual application conditions. It integrates sensor data with models that enable in situ assessment of the deviation or degradation of a product from an expected normal operating condition and the prediction of the future state of reliability. This article presents a formal implementation procedure, which includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life-cycle loads, damage accumulation, and assessment of uncertainty. Applications of PoF-based prognostics and health management are also discussed. Keywords: reliability; prognostics; physics of failure; design-for-reliability; reliability prediction