Search result: Catalogue data in Autumn Semester 2023
Electrical Engineering and Information Technology Bachelor  | ||||||||||||||||||||||||||||||||||||||||||||||||
 Laboratory Courses, Projects, SeminarsA minimum of 15 cp must be achieved in the category "Laboratory Courses, Projects, Seminars | ||||||||||||||||||||||||||||||||||||||||||||||||
| Projects & Seminars Enrolment is only possible for students in the BSc Electrical Engineering and Information Technology from Friday before the start of the semester. Places are allocated using the P&S application tool (https://psapp.ee.ethz.ch/). Please only enrol for P&S for which you apply via the tool. | ||||||||||||||||||||||||||||||||||||||||||||||||
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| 227-0085-51L | P&S: Programming Heterogeneous Computing Systems with GPUs and other Accelerators  Does not take place this semester. Course can only be registered for once. A repeatedly registration in a later semester is not chargeable. | W | 3 credits | 3P | ||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The increasing difficulty of scaling the performance and efficiency of CPUs every year has created the need for turning computers into heterogeneous systems, i.e., systems composed of multiple types of processors that can suit better different types of workloads or parts of them. More than a decade ago, Graphics Processing Units (GPUs) became general-purpose parallel processors, in order to make their outstanding processing capabilities available to many workloads beyond graphics. GPUs have been critical key to the recent rise of Machine Learning and Artificial Intelligence, which took unrealistic training times before the use of GPUs. Field-Programmable Gate Arrays (FPGAs) are another example computing device that can deliver impressive benefits in terms of performance and energy efficiency. More specific examples are (1) a plethora of specialized accelerators (e.g., Tensor Processing Units for neural networks), and (2) near-data processing architectures (i.e., placing compute capabilities near or inside memory/storage). Despite the great advances in the adoption of heterogeneous systems in recent years, there are still many challenges to tackle, for example: - Heterogeneous implementations (using GPUs, FPGAs, TPUs) of modern applications from important fields such as bioinformatics, machine learning, graph processing, medical imaging, personalized medicine, robotics, virtual reality, etc. - Scheduling techniques for heterogeneous systems with different general-purpose processors and accelerators, e.g., kernel offloading, memory scheduling, etc. - Workload characterization and programming tools that enable easier and more efficient use of heterogeneous systems. If you are enthusiastic about working hands-on with different software, hardware, and architecture projects for heterogeneous systems, this is your P&S. You will have the opportunity to program heterogeneous systems with different types of devices (CPUs, GPUs, FPGAs, TPUs), propose algorithmic changes to important applications to better leverage the compute power of heterogeneous systems, understand different workloads and identify the most suitable device for their execution, design optimized scheduling techniques, etc. In general, the goal will be to reach the highest performance reported for a given important application. Prerequisites of the course: - Digital Circuits AND Computer Engineering (or equivalent courses) - Familiarity with C/C++ programming and strong coding skills. - Interest in future computer architectures and computing paradigms. - Interest in discovering why things do or do not work and solving problems - Interest in making systems efficient and usable The course is conducted in English. The course has two main parts: 1. Short weekly lectures on GPU and heterogeneous programming. 2. Hands-on project: Each student develops his/her own project. | |||||||||||||||||||||||||||||||||||||||||||||||
| Content | See: https://safari.ethz.ch/projects_and_seminars/doku.php?id=heterogeneous_systems for past examples. | |||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | See: https://safari.ethz.ch/projects_and_seminars/doku.php?id=heterogeneous_systems | |||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Learning Materials ============ 1. An introduction to SIMD processors and GPUs: http://www.youtube.com/watch?v=hOeIkAYraTE 2. An introduction to GPUs and heterogeneous programming: http://www.youtube.com/watch?v=y40-tY5WJ8A 3. Example recent studies of FPGA and GPU implementation for bioinformatics: GateKeeper: FPGA for bioinformatics (Bioinformatics 2017): Link SneakySnake: Pre-alignment filter on FPGA and GPU (Bioinformatics 2020): Link 4. An example recent study of a suite of heterogeneous benchmarks: Chai: heterogeneous benchmarks (ISPASS 2017): https://chai-benchmarks.github.io/assets/ispass17.pdf 5. An example recent study of a medical image application on GPU: GPU for medical imaging (CMPB 2020): Link 6. Example studies of programming tools and performance portability on heterogeneous systems: Boyi: execution models for FPGAs (FPGA 2020): Link Zorua: hardware support for GPU performance portability (MICRO 2016): Link Locality descriptor: Cross-layer abstraction to express data locality on GPUs (ISCA 2018): Link 7. Example studies of scheduling techniques for heterogeneous systems: Thread scheduling (MICRO 2011): https://people.inf.ethz.ch/omutlu/pub/large-gpu-warps_micro11.pdf DASH: memory scheduling (TACO 2016): Link | |||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites of the course: - Digital Circuits AND Computer Engineering (or equivalent courses). - Familiarity with C/C++ programming and strong coding skills. - Interest in future computer architectures and computing paradigms. - Interest in discovering why things do or do not work and solving problems - Interest in making systems efficient and usable | |||||||||||||||||||||||||||||||||||||||||||||||
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| 227-0085-53L | P&S: Motion Sensing Technologies for Magnetic Resonance Imaging (MRI) Does not take place this semester. Course can only be registered for once. A repeatedly registration in a later semester is not chargeable. | W | 4 credits | 4P | K. P. Prüssmann | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Current MRI scans are limited by patient motion. In clinics, radiologists are often confronted with images with severe motion artefacts in their images. They either have to make a diagnosis although the image artefacts were they could miss crucial information, or they have to send the patient back into the scanner for reacquisition. Such reacquisition might inflict additional costs in the six-figure range per scanner per year. Further, in research, MRI images from ultra-high field systems are already limited by motion from the cardiobalistic and respiratory movement. Resulting in subpar performance if not addressed appropriately. The key to overcoming such motion artefacts is estimating the motion and correct for it. Preferably this is done prospective in real-time or otherwise afterwards retrospective in the image reconstruction. Such methods are instrumental in brain imaging since the brain's movement is well described by the rigid body behaviour of the skull. To do such motion correction, one needs a motion-sensing technology to measure the movement of the human skull with high precision, accuracy and temporal resolution. All this has to be done while being integrated into an MRI machine where powerful static magnetic fields are present, kW of pulsed RF power and MVA of changing magnetic field gradients are present. In this P&S we explore different motion sensing technologies suitable for deployment in an MRI machine. What you can expect is that we discuss the theory of multiple sensing technologies and then implement an optical, shortwave RF and NMR phase motion sensor. We will spend most of our time in the lab constructing such sensors and testing them on our robotic test bench. Finally, we would also experiment in our MRI facilities, where we would perform motion correction experiments. | |||||||||||||||||||||||||||||||||||||||||||||||
| 227-0085-54L | P&S: Optics and Spectroscopy Lab Does not take place this semester. Course can only be registered for once. A repeatedly registration in a later semester is not chargeable. | W | 3 credits | 4P | J. Leuthold | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of this P&S is to learn the basics of working with optics and how to assemble optical systems. It is intended to show the practical side to the many optics lectures that are offered at D-ITET. The course will give a very brief introduction on laser safety, basic building blocks for optics and information on how to handle such elements. The following classes allow the students to test very basics properties of lenses and lasers and how the corresponding optomechanics can be used to arrange a simple setup. After this, the different student groups rotate through four different experiments where they get the chance to build and align different optical setups and perform various measurements. No prior knowledge is required. | |||||||||||||||||||||||||||||||||||||||||||||||
| 227-0085-56L | P&S: Intelligent Architectures via Hardware/Software Cooperation Does not take place this semester. Course can only be registered for once. A repeatedly registration in a later semester is not chargeable. | W | 3 credits | 3P | O. Mutlu | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Modern general-purpose processors are agnostic to an application 19s high-level semantic information. Hence, they employ prediction-based techniques to enable computational and memory optimizations, such as prefetching, cache management policies, memory data placement, instruction scheduling, and many others. As such, the potential of such optimizations is limited due to the limited information the underlying hardware can discover on its own and such optimizations come with large area, power and complexity overheads required by the hardware for prediction purposes. Purely-hardware optimizations cannot achieve their performance potential and waste power, complexity and hardware area, since they are not aware of the application characteristics. On the other hand, purely-software optimizations are fundamentally tied up and limited by the underlying hardware. A promising way to increase the performance of modern applications is to co-design software and hardware. Hence, lately both industry and academia are making serious attempts to improve performance, energy and security using hardware/software cooperative schemes such as application-specific hardware accelerators (e.g., Google 19s Tensor Processing Unit) and application-specific extensions in general-purpose processors (e.g., Media Engine in Apple M1). In this course, we will explore several different topics around hardware/software co-design such as: (i) new hardware/software interfaces (e.g., virtual memory, instruction set architecture) to enhance performance, energy and security, (ii) hardware/software co-design schemes to improve the performance of the memory subsystem in killer memory-intensive applications (e.g., sparse and irregular workloads), (iii) hardware/software cooperative machine-learning-based techniques for different microarchitectural components such as prefetchers, caches and branch predictors, which would continuously learn from the vast amount of memory accesses seen by a processor and adapt to the varying workload and system conditions. If you are enthusiastic about working hands-on to design both software and hardware, this is your P&S. You will have the opportunity to study modern applications, propose software changes to better match the underlying hardware components, design new hardware components that better match the overlying software and come up with new machine-learning techniques to design efficient microarchitectural components. You will also learn how to program industry-supported microarchitectural simulators and study the performance of modern workloads after your hardware/software modifications. Prerequisites of the course: - Digital Circuits AND Computer Engineering (or equivalent courses) - Familiarity with C/C++ programming and strong coding skills. - Interest in future computer architectures and computing paradigms. - Interest in discovering why things do or do not work and solving problems - Interest in making systems efficient and usable Preferable: - Hands-on experience with Machine Learning frameworks (depends on the topic you choose) The course is conducted in English. | |||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | See: https://safari.ethz.ch/projects_and_seminars/ | |||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Learning materials ============ [1] Onur Mutlu,"Intelligent Architectures for Intelligent Machines" Invited Keynote Paper in Proceedings of the 2020 International Symposia on VLSI (VLSI): Link [2] Kanellopoulos et al. "SMASH: Co-designing Software Compression and Hardware-Accelerated Indexing for Efficient Sparse Matrix Operations", Proceedings of the 52nd International Symposium on Microarchitecture (MICRO 2019): Link [3] Bera et al. "Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning" Proceedings of the 54th International Symposium on Microarchitecture (MICRO 2021): Link [4] Hajinazar et al. "The Virtual Block Interface: A Flexible Alternative to the Conventional Virtual Memory Framework" Proceedings of the 47th International Symposium on Computer Architecture (ISCA 2020): https://people.inf.ethz.ch/omutlu/pub/VBI-virtual-block-interface_isca20.pdf [5] Vijaykumar et al. "A Case for Richer Cross-layer Abstractions: Bridging the Semantic Gap with Expressive Memory", Proceedings of the 45th International Symposium on Computer Architecture (ISCA 2018): Link [6] Vijaykumar et al. “MetaSys: A Practical Open-Source Metadata Management System to Implement and Evaluate Cross-Layer Optimizations” TACO 2022: https://arxiv.org/abs/2105.08123 [7] Vijaykumar et al. "The Locality Descriptor: A Holistic Cross-Layer Abstraction to Express Data Locality in GPUs" Proceedings of the 45th International Symposium on Computer Architecture (ISCA 2018): Link [8] Besta et al. "SISA: Set-Centric Instruction Set Architecture for Graph Mining on Processing-in-Memory Systems", Proceedings of the 54th International Symposium on Microarchitecture (MICRO 2021): https://people.inf.ethz.ch/omutlu/pub/SISA-GraphMining-on-PIM_micro21.pdf | |||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites of the course: - Digital Circuits AND Computer Engineering - Familiarity with C/C++ programming and strong coding skills. - Interest in future computer architectures and computing paradigms. - Interest in discovering why things do or do not work and solving problems - Interest in making systems efficient and usable Preferable: - Hands-on experience with Machine Learning frameworks (depends on the topic you choose) | |||||||||||||||||||||||||||||||||||||||||||||||
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| 227-0085-57L | P&S: Wearable Ultrasound: Tools and Technologies The course unit can only be taken once. Repeated enrollment in a later semester is not creditable. | W | 3 credits | 3P | A. Cossettini | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Ultrasound is one of the most used medical imaging techniques and it enables many applications, including the monitoring of musculoskeletal activity during movement, the imaging of carotid artery, and the control of prosthetic devices for human-machine interfaces. Recent developments showcased wearable ultrasound probes operating at minimal power consumption, enabling multi-day continuous monitoring of physiological parameters, and many companies and research centers are actively working on the development of the next generation of truly-wearable ultrasound for a number of monitoring and diagnostics applications. To sustain such recent developments, it is important to be familiar with all sub-components (hardware and software) of such biomedical systems. The goal of this course is the development of the main skills required for successfully developing a wearable ultrasound probe. The students will learn about ultrasound basics, transducer control, analog front-end/analog-to-digital converter configurations, signal processing for ultrasound, beamforming and generation of images, microcontroller-based wireless communication, and practical procedures for performing ultrasound experiments. The course will also introduce the students to Python (applied to ultrasound signal processing) and will include a crash course on Nordic (nRF52 family) microcontrollers. In the final weeks of the course, the students will work on an assigned project. The course will be taught in English. | |||||||||||||||||||||||||||||||||||||||||||||||
| Content | Ultrasound is one of the most used medical imaging techniques and it enables many applications, including the monitoring of musculoskeletal activity during movement, the imaging of carotid artery, and the control of prosthetic devices for human-machine interfaces. Recent developments showcased wearable ultrasound probes operating at minimal power consumption, enabling multi-day continuous monitoring of physiological parameters, and many companies and research centers are actively working on the development of the next generation of truly-wearable ultrasound for a number of monitoring and diagnostics applications. To sustain such recent developments, it is important to be familiar with all sub-components (hardware and software) of such biomedical systems. | |||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | - Can use the Linux-Terminal (e.g. navigating folder structure) - Interest in biomedical applications - bring your own laptop, 20GB of free disk space | |||||||||||||||||||||||||||||||||||||||||||||||
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| 227-0085-58L | P&S: Autonomous Cars and Robots The course unit can only be taken once. Repeated enrollment in a later semester is not creditable. | W | 4 credits | 4P | M. Magno | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Autonomous mobile robotics is a promising field that spans from food delivery robots to the Perseverance Mars rover. In this P&S you will be introduced to the fundamental building blocks of robotics, by hands on experience in the context of the F1TENTH autonomous racing and the Robot Operating System (ROS)! Autonomous racing pushes the boundaries in algorithmic design and implementation in the fields of perception, planning and control. Thus it serves researchers as a limits test for autonomous driving and is an important building step in the field of general self driving and AI. F1TENTH is an open-source autonomous racing competition involving a racing car in the scale of 1:10. This P&S allows you to apply hands-on robotics and is the right fit for you if you want to further delve into this fascinating field of embedded systems, perception, planning and control. Lastly, you will get experience in the widely used ROS framework. | |||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | - Can use the Linux-Terminal (e.g. navigating folder structure and ssh) - Python (e.g. basic loops, OOP) - Interest in autonomous driving - 20GB of free space on your laptop | |||||||||||||||||||||||||||||||||||||||||||||||
| 227-0085-59L | P&S: Hands-On Deep Learning Course can only be registered for once. A repeatedly registration in a later semester is not chargeable. | W | 2 credits | 2P | R. Wattenhofer | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | This lab introduces deep learning through the PyTorch framework in a series of hands-on exercises, exploring topics in computer vision, natural language processing, audio processing, graph neural networks, and representation learning. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This P&S introduces deep learning through the PyTorch framework in a series of hands-on examples, exploring topics in computer vision, natural language processing, graph neural networks, and representation learning. With the objective to expose students to both common and cutting-edge neural architectures and to build intuition about their inner working by the means of examples. Students learn about various network structures as building blocks and use them to solve worked examples and course challenges. After attending this course, students will be familiar with multi-layer perceptrons, convolutional neural networks, recurrent neural networks, transformer encoders, graph convolutional/isomorphism/attention networks, and autoencoders. | |||||||||||||||||||||||||||||||||||||||||||||||
| Content | This lab introduces deep learning through the PyTorch framework in a series of hands-on exercises, exploring topics in computer vision, natural language processing, audio processing, graph neural networks, and representation learning. | |||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Python Notebooks will be distributed to students before every session. | |||||||||||||||||||||||||||||||||||||||||||||||
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| 227-0085-63L | P&S: Enabling Smart and Low Power IoT Sensor Nodes Course can only be registered for once. A repeatedly registration in a later semester is not chargeable. | W | 4 credits | 4P | T. Polonelli, M. Magno | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Enabling smart and low-power IoT sensor nodes – Firmware programming, sensor acquisition and signal processing, digital interfaces, wireless connectivity (Bluetooth and WiFi) combined with an onboard Neural Network (NN) classifier. Microprocessors (MCU) are everywhere today, from ultra-low power wearable devices to robots and embedded systems for the industry. In general, combining an MCU with sensors, a wireless interface, and onboard signal processing is the foundation for most electronic devices. In this practical course, the students will have the opportunity to improve their C programming skills on an actual device, with several sensors (microphones, accelerometers, vibrometers, temperature, humidity), a dual Bluetooth-WiFi wireless interface, and an AI accelerator for onboard data analysis and processing. The kit used in this course is directly provided by STMicroelectronics and can be found here: https://www.st.com/en/evaluation-tools/steval-stwinkt1.html. Combining theory (20%) and practical implementation (80%) should enable students to conduct high-level firmware programming for microcontrollers. After seven practical exercises and hands-on lessons, students will have the opportunity to propose and implement their own idea making use of the previously acquired knowledge and the supervisor's support. The primary programming language will be C. A basic knowledge of Python is suggested but optional. The course will be taught in English. | |||||||||||||||||||||||||||||||||||||||||||||||
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| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||
| 227-0091-10L | Group Project I | W | 6 credits | 5A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | Students must work in groups in supervised projects for 150 to 180 hours minimum. The topics of the group work are open and can be technical of specific nature or more general in the context of engineering. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | see above | |||||||||||||||||||||||||||||||||||||||||||||||
| 227-0092-10L | Group Project II | W | 6 credits | 5A | Lecturers | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | Students must work in groups in supervised projects for 150 to 180 hours minimum. The topics of the group work are open and can be technical of specific nature or more general in the context of engineering. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | see above | |||||||||||||||||||||||||||||||||||||||||||||||
| Internship in Industry The internship in industry can only be enrolled for during bachelor's studies according to the 2016 regulations. According to the 2018 regulations, an internship in industry can be taken at master's level. Please note the conditions for internships in industry as set forward by the "Guidelines for the "Laboratory Courses - Projects - Seminars ", see Link (German only). | ||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||
| 227-0093-10L | Internship in Industry Only for students in the Bachelor's Programme Electrical Engineering and Information Technology, Regulations 2016. For students enrolled in the 2018 Programme Regulations, see "227-1550-10L Internship in Industry" at Master's level. | W | 6 credits | external organisers | ||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The main objective of the 12-week internship is to expose bachelor's students to the industrial work environment. During this period, students have the opportunity to be involved in on-going projects at the host institution. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | see above | |||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Please note the conditions for Internships in industry as set forward by the "Guidelines for the "Laboratory Courses - Projects - Seminars ", see Link (German only). | |||||||||||||||||||||||||||||||||||||||||||||||
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| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||
| 227-0651-00L | Applied Circuit and PCB-Design | W | 2 credits | 4G | A. Blanco Fontao | |||||||||||||||||||||||||||||||||||||||||||
| Abstract | Participants learn how to design a predefined electronic circuit and how to lay out the pertaining circuit board. CAE and CAD activities for design and simulation are carried out with the aid of Altium Designer. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal is to become acquainted with all those practical aspects of electronic circuit and PCB design by working through a modest but complete application example. This involves analysis of specifications, the evaluation of electronic parts, efficient testing and failure search, electromagnetic compatibility (EMC), the usage of industrial CAE/CAD tools for circuit simulation and PCB layout, generating production data for the board manufacturer, board mounting, testing and start up. | |||||||||||||||||||||||||||||||||||||||||||||||
| Content | Content: - Development - from the idea to the final product - Analysis of given circuit specifications - Searching the Internet for electronics parts - Choosing electronic parts: avoiding mistakes - Setting up the Altium Designer environment - Structure of component libraries - Preparing schematic symbols for CAE - Preparing footprints for CAD - Linking component libraries and databases - Introduction to Concord Pro and Supply Chain Management - Structure of schematic diagrams and circuits - Assigning schematic functions to physical parts - Capturing a predefined circuit - Hints for improved testing and failure analysis - Checking schematic data - Simulation of mixed-signal circuits using Spice - Introduction to PCB manufacturing - Turning circuit schematics into a workable layout using Altium Designer - Component placement on the PCB - Manual and automatic interconnect routing - Design for EMC and High-Speed - Preparation of production data for the board manufacturer - Documentation for manufacturing and assembly - PCB assembly (component mounting and soldering) - Final circuit testing and start-up. | |||||||||||||||||||||||||||||||||||||||||||||||
| Literature | All necessary documents will be available as electronic documents (PDF). | |||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | - The course is recommended to all students who plan to design an electronic circuit or a PCB in an upcoming term project or as part of their master thesis. Attending this course during the term before will ensure they are optimally prepared and will allow them to fully focus on their project. - The number of participants is limited. - For their own students and staff, the Department of Information Technology and Electrical Engineering provides electronic components and consumables free of charge. All other participants have to bear a 200 CHF fee for those items. | |||||||||||||||||||||||||||||||||||||||||||||||
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