A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM

Over recent years, the manufacturing industry has seen constant growth and change. From one side, it has been affected by the fourth industrial revolution (Industry 4.0). From the other side, it has had to enhance its ability to meet higher customer expectations, such as producing more customized products in a shorter time. In the contemporary competitive market of manufacturing, quality is a criterion of primary importance for winning market share. Quality improvement must be coupled with a concern for high performance. One of the most promising concepts for quality control and improvement is called zero defect manufacturing (ZDM), which utilizes the benefits of Industry 4.0 technologies. ZDM imposes the rule that any event in the production process should have a counter-action to mitigate it. In light of this, the current research developed a methodology the manufacturer can use to correctly select or design appropriate ZDM strategies and equipment to implement at each manufacturing stage. This methodology consists of several steps. The first step is to conduct several simulations using a dynamic scheduling tool with specific data sets to develop a digital twin (DT). The data sets are created using the Taguchi design of experiments methodology. The DT model is created for use in predicting the results of the developed scheduling tool without actually using said tool. Using the DT, multiple ZDM parameter-combination sets can be created and plugged into the model. This process generates ZDM performance maps that show the effect of each ZDM strategy at each manufacturing stage under different control parameters. These maps are intended to provide information for comparing different ZDM-oriented equipment to help manufacturers reach a final decision on correct and efficient ZDM implementation or to assist in the design phase of a ZDM strategy implementation.

On the dependability of nanoscale circuits and systems

Milos Stanisavljevic

The invention of the integrated circuit and the manufacturing progress as well as continuing progress in the manufacturing process are the fundamental engines for the implementation of all technologies that support today's information society. The vast majority of microelectronic applications presented nowadays use the well-established CMOS process and fabrication technology which exhibit high reliability rates. The hypothesis of reliable components has mostly been taken in the development of electronic systems fabricated in the past four decades. The steady downscaling of CMOS technology has led to the development of devices with nanometer dimensions. For future nano-circuits, emerging nanodevices and their associated interconnects, the expected higher probabilities of failures, as well as the higher sensitivities to noise and variations, could make future chips prohibitively unreliable. The systems to be fabricated will be made of unreliable components and achieving 100% correctness will not be only extremely costly, but might be plainly impossible. The global picture is that reliability emerges as one of the most significant threats to the design of future integrated computing systems. Building reliable systems out of unreliable components will require increased cooperative involvement of the logic designers and architects, where high-level techniques will rely upon lower levels support based on novel modeling including component and system reliability as design parameters. An architecture suitable for circuit-level and gate-level redundant modules and exhibiting significant immunity to permanent and random failures, as well as unwanted fluctuation of the fabrication parameters is presented, which is based on a four-layer feed-forward topology, using averaging and thresholding as the core voter mechanisms. The architecture with both fixed and adaptable threshold is compared to triple and R-fold modular redundancy techniques, and its superiority is demonstrated based on numerical simulations as well as analytical developments. A chip implementation of the architecture is realized. Other applications of the architecture like delay variations minimization are identified and explored. A novel general method enabling introduction of fault-tolerance, and evaluation of circuit and architecture reliability is proposed. The method is based on the modeling of probability density functions (PDFs) of unreliable components and their subsequent evaluation for a given reliability architecture. PDF modeling, presented for the first in the context of realistic technology and arbitrary circuit size, is based on a cutting-edge reliability evaluation algorithm and offers scalability, speed and accuracy. Fault modeling has also been developed to support PDF modeling. In the second part of the thesis a new methodology that introduces reliability in existing design flows is proposed. The methodology consists of partitioning the whole system into reliability-optimal partitions and applying reliability evaluation and optimization at local and system level. System level reliability improvement of different fault-tolerant techniques is studied in depth. Optimal partition size analysis and redundancy optimization have been performed for the first time in the context of a large-scale system, showing that a target reliability can be achieved with low to moderate redundancy factors (R < 50) even for high defect densities (device failure rate up to 10-3). The optimal window of application of each fault-tolerant technique with respect to defect density is presented as a way to find the optimum design trade-off between the reliability and power/area. R-fold modular redundancy with distributed voting and averaging voter is selected as the most promising candidate for the implementation in trillion-transistor logic systems. Finally, a realistic circuit example of the methodology implementation is verified using simulations.

EPFL2009

Design Methodology and Innovative Device Concept for Acoustic Hearing Implants

Hans Bernhard

Nowadays, hearing impaired people can be treated with a multitude of different devices and surgical interventions that are selected in function of the type and degree of hearing loss of the patient. These known therapies cover the major part of possible hearing losses except severe to profound mixed hearing loss. The present thesis results from an industrial project that aims to develop a dedicated hearing therapy that treats this kind of hearing loss based on direct acoustic cochlear stimulation (DACS). A semi-implantable investigational hearing device is used in a clinical trial with four patients to validate this novel therapy approach. The development of this investigational hearing implant presents a considerable challenge due to the numerous medical and engineering aspects that have to be considered. The conceptual design process is therefore of central importance because it incorporates all the relevant aspects and defines the future device characteristics and performances. Fundamental design aspects that are not considered at this early design stage can only be implemented with difficulties and increased efforts later on. This is the motivation to develop within the scope of this thesis a conceptual design methodology that supports and facilitates this essential design phase. A conceptual design methodology for acoustic hearing implants is therefore presented at the example of the DACS investigational device. The methodology consists of a four-step-procedure and a system diagram tool. The four steps of the procedure are: Step 1: determination and analysis of the human body functions that shall be emulated by the system Step 2: determination of the preliminary system architecture Step 3: determination of the user and system requirements Step 4: conceptual design The system diagram tool guides the engineer through all four steps and helps visualizing and structuring the system environments, functions, interfaces and, most important, all interactions between the system elements and environments. The diagram drawing rules ensure that all requirements coming from the environments are correctly linked with the concerned system functions and therefore ensure that the system meets all these requirements. Finally, the system diagram is a valuable tool for the conceptual design step. Because of the clearly represented system elements and interactions, it supports the development of integrated solutions. The output of the presented methodology is a design concept that cannot be directly quantified to assess the benefit of the methodology. Only once the resulting device is successfully verified against the system requirements and validated against the user requirements, it can be concluded that the underlying concept is adequate. The outcome of the clinical study shows that the four implanted patients benefit all from the DACS device, which is therefore well suited to treat severe to profound mixed hearing loss. This confirms that device meets the user requirements and implies that conceptual design methodology is adequate. The realized investigational device consists of an externally worn audio processor, a percutaneous connector, and an implantable actuator. The hermetically sealed balanced armature actuator provides a maximal equivalent sound pressure level of 125 dB over the frequency range between 100 Hz and 10'000 Hz with a limited 1-mW power supply. A lumped parameter model of the actuator dynamics and a finite element model of the electro-mechanic actuator are used for the detailed actuator design.

EPFL2011

Localizing the Source of an Epidemic Using Few Observations

Brunella Marta Spinelli

Localizing the source of an epidemic is a crucial task in many contexts, including the detection of malicious users in social networks and the identification of patient zeros of disease outbreaks. The difficulty of this task lies in the strict limitations on the data available: In most cases, when an epidemic spreads, only few individuals, who we will call sensors, provide information about their state. Furthermore, as the spread of an epidemic usually depends on a large number of variables, accounting for all the possible spreading patterns that could explain the available data can easily result in prohibitive computational costs. Therefore, in the field of source localization, there are two central research directions: The design of practical and reliable algorithms for localizing the source despite the limited data, and the optimization of data collection, i.e., the identification of the most informative sensors. In this dissertation we contribute to both these directions. We consider network epidemics starting from an unknown source. The only information available is provided by a set of sensor nodes that reveal if and when they become infected. We study how many sensors are needed to guarantee the identification of the source. A set of sensors that guarantees the identification of the source is called a double resolving set (DRS); the minimum size of a DRS is called the double metric dimension (DMD). Computing the DMD is, in general, hard, hence estimating it with bounds is desirable. We focus on G(N,p) random networks for which we derive tight bounds for the DMD. We show that the DMD is a non-monotonic function of the parameter p, hence there are critical parameter ranges in which source localization is particularly difficult. Again building on the relationship between source localization and DRSs, we move to optimizing the choice of a fixed number K of sensors. First, we look at the case of trees where the uniqueness of paths makes the problem simpler. For this case, we design polynomial time algorithms for selecting K sensors that optimize certain metrics of interest. Next, turning to general networks, we show that the optimal sensor set depends on the distribution of the time it takes for an infected node u to infect a non-infected neighbor v, which we call the transmission delay from u to v. We consider both a low- and a high-variance regime for the transmission delays. We design algorithms for sensor placement in both cases, and we show that they yield an improvement of up to 50% over state-of-the-art methods. Finally, we propose a framework for source localization where some sensors (called dynamic sensors) can be added while the epidemic spreads and the localization progresses. We design an algorithm for joint source localization and dynamic sensor placement; This algorithm can handle two regimes: offline localization, where we localize the source after the epidemic spread, and online localization, where we localize the source while the epidemic is ongoing. We conduct an empirical study of offline and online localization and show that, by using dynamic sensors, the number of sensors we need to localize the source is up to 10 times less with respect to a strategy where all sensors are deployed a priori. We also study the resistance of our methods to high-variance transmission delays and show that, even in this setting, using dynamic sensors, the source can be localized with less than 5% of the nodes being sensors.

EPFL2018