AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Predicting Electrical Faults in Power Distribution Network

Electricity is becoming increasingly important in modern civilization, and as a result, the emphasis on and use of power infrastructure is gradually expanding. Simultaneously, investment and distribution modes are shifting from the large-scale centralized generation of electricity and sheer consumption to decentralized generators and extremely sophisticated clients. This transformation puts further strain on old infrastructure, necessitating significant expenditures in future years to ensure a consistent supply. Subsequent technical and prediction technologies can help to maximize the use of the current grid while lowering the probability of faults. This study discusses some of the local grid difficulties as well as a prospective maintenance and failure probabilistic model. To provide an effective and convenient power source to consumers, a high Volta protects and maintains under fault conditions. Most of the fault identification and localization approaches rely on real and reactive power converter observations of electronic values. This can be seen in metrics and ground evaluations derived via internet traffic. This paper provides a thorough examination of the mechanisms for error detection, diagnosis, and localization in overhead lines. The proposal is then able to make suggestions about the ways that can be incorporated to predict foreseen faults in the electrical network. The three classifiers, Random Forest, XGBoost and Decision tree are producing high accuracies, while Logistic Regression and SVM are producing realistic accuracy results

DeSyRe: on-Demand System Reliability

The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints

Metal-organic framework-based biosensing platforms for the sensitive determination of trace elements and heavy metals: A comprehensive review

Heavy metals in food and water sources are potentially harmful to humans. Determination of these pollutants is critical for improving safety. Effective recognition systems are a contemporary challenge; several novel technologies for the quick, easy, selective, and sensitive determination of these compounds are in demand. Metal-organic framework (MOF)-based sensors and biosensors have crucial applications in identifying these potentially harmful substances. Here, we review electrochemical and optical biosensors for in situ sensing that are sensitive and cost effective, with a simple protocol and wide linear range. Despite the abundance of articles in this field, we assessed and checked out various basic features of MOFs as porous compounds that include clusters or ions, and some of the ligands connected to these clusters have a variety of useful properties. Afterward, we also assessed various electrochemical and optical sensing assays, which have recently gathered interest because of their potential applications for recognizing certain compounds in the environment. Their operation and approaches are dependent on their structures, the materials and component types used, and the substances they are targeting

A Review of Bayesian Methods in Electronic Design Automation

The utilization of Bayesian methods has been widely acknowledged as a viable solution for tackling various challenges in electronic integrated circuit (IC) design under stochastic process variation, including circuit performance modeling, yield/failure rate estimation, and circuit optimization. As the post-Moore era brings about new technologies (such as silicon photonics and quantum circuits), many of the associated issues there are similar to those encountered in electronic IC design and can be addressed using Bayesian methods. Motivated by this observation, we present a comprehensive review of Bayesian methods in electronic design automation (EDA). By doing so, we hope to equip researchers and designers with the ability to apply Bayesian methods in solving stochastic problems in electronic circuits and beyond.Comment: 24 pages, a draft version. We welcome comments and feedback, which can be sent to [email protected]

Fault-based Analysis of Industrial Cyber-Physical Systems

The fourth industrial revolution called Industry 4.0 tries to bridge the gap between traditional Electronic Design Automation (EDA) technologies and the necessity of innovating in many indus- trial fields, e.g., automotive, avionic, and manufacturing. This complex digitalization process in- volves every industrial facility and comprises the transformation of methodologies, techniques, and tools to improve the efficiency of every industrial process. The enhancement of functional safety in Industry 4.0 applications needs to exploit the studies related to model-based and data-driven anal- yses of the deployed Industrial Cyber-Physical System (ICPS). Modeling an ICPS is possible at different abstraction levels, relying on the physical details included in the model and necessary to describe specific system behaviors. However, it is extremely complicated because an ICPS is com- posed of heterogeneous components related to different physical domains, e.g., digital, electrical, and mechanical. In addition, it is also necessary to consider not only nominal behaviors but even faulty behaviors to perform more specific analyses, e.g., predictive maintenance of specific assets. Nevertheless, these faulty data are usually not present or not available directly from the industrial machinery. To overcome these limitations, constructing a virtual model of an ICPS extended with different classes of faults enables the characterization of faulty behaviors of the system influenced by different faults. In literature, these topics are addressed with non-uniformly approaches and with the absence of standardized and automatic methodologies for describing and simulating faults in the different domains composing an ICPS. This thesis attempts to overcome these state-of-the-art gaps by proposing novel methodologies, techniques, and tools to: model and simulate analog and multi-domain systems; abstract low-level models to higher-level behavioral models; and monitor industrial systems based on the Industrial Internet of Things (IIOT) paradigm. Specifically, the proposed contributions involve the exten- sion of state-of-the-art fault injection practices to improve the ICPSs safety, the development of frameworks for safety operations automatization, and the definition of a monitoring framework for ICPSs. Overall, fault injection in analog and digital models is the state of the practice to en- sure functional safety, as mentioned in the ISO 26262 standard specific for the automotive field. Starting from state-of-the-art defects defined for analog descriptions, new defects are proposed to enhance the IEEE P2427 draft standard for analog defect modeling and coverage. Moreover, dif- ferent techniques to abstract a transistor-level model to a behavioral model are proposed to speed up the simulation of faulty circuits. Therefore, unlike the electrical domain, there is no extensive use of fault injection techniques in the mechanical one. Thus, extending the fault injection to the mechanical and thermal fields allows for supporting the definition and evaluation of more reliable safety mechanisms. Hence, a taxonomy of mechanical faults is derived from the electrical domain by exploiting the physical analogies. Furthermore, specific tools are built for automatically instru- menting different descriptions with multi-domain faults. The entire work is proposed as a basis for supporting the creation of increasingly resilient and secure ICPS that need to preserve functional safety in any operating context

The 2021 flexible and printed electronics roadmap

This roadmap includes the perspectives and visions of leading researchers in the key areas of flexible and printable electronics. The covered topics are broadly organized by the device technologies (sections 1–9), fabrication techniques (sections 10–12), and design and modeling approaches (sections 13 and 14) essential to the future development of new applications leveraging flexible electronics (FE). The interdisciplinary nature of this field involves everything from fundamental scientific discoveries to engineering challenges; from design and synthesis of new materials via novel device design to modelling and digital manufacturing of integrated systems. As such, this roadmap aims to serve as a resource on the current status and future challenges in the areas covered by the roadmap and to highlight the breadth and wide-ranging opportunities made available by FE technologies

A Review of Aeronautical Fatigue Investigations in Finland May 2021 - April 2023

This document was prepared for the delivery to the 38th Conference of the International Committee on Aeronautical Fatigue and Structural Integrity (ICAF) scheduled to be held in Delft, The Netherlands, 26-29 June 2023.A review is given of the aircraft structural fatigue research and associated activities which form part of the programs within the Air Force Command Finland (AFCOMFIN), the Finnish Defence Force Logistics Command, Joint Systems Centre (FDFLOGCOM JSC), Air Systems Division; Army Command Finland (ARCOMFIN); Aalto University; Arecap Ltd; Elomatic Ltd; Emmecon Ltd; Eurofins Expert Services Oy; Insta ILS Oy; Patria Aviation Oy; Tampere University; Trano Oy; Trueflaw Ltd; and VTT Technical Research Centre of Finland Ltd (VTT).The review summarizes fatigue related research programs and investigations on specific military aircraft since the previous Finnish National Review compiled for the International Committee on Aeronautical Fatigue and Structural Integrity (ICAF) webinar, 30 June 2021