Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course EITP25F valid from Spring 2020

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  • English
  • Every spring semester
  • The purpose of this course is to give an in depth understanding for the physics of common memory device technologies with focus on non-volatile memories. Furthermore, the course covers how these memory devices can be integrated to create neuromorphic hardware for applications in machine learning and artificial intelligence. Finally, the course gives an introduction to the architectures and algorithms that are used in machine learning, to give a basic understanding for the needs that memory devices and their connections need to fulfil.
  • Memory devices of the computer: SRAM, DRAM, NAND

    Non-volatile memory devices: The memristor. Resistive memories (RRAM), phase change memories (PCM), ferroelectric memories (FeRAM), magnetic memories (MRAM).

    Integration of memory devices: 3D stacking for scalability, crossbar architecture

    Neural network architectures: Feed forward neural networks, recursive networks. spiking neural networks.

    Machine learning algorithms: Backpropagation, gradient descent, Hebbian and non-Hebbian learning, unsupervised learning through STDP.
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • Explain in general the memory hierarchy of a modern computer and in detail how its memory components function.
    Explain the physical processes that determine the functionality of common non-volatile memory types such as RRAM, PCM, FeRAM and STT-MRAM.
    Understand in general the limitations and benefits of the various memory technologies treated in the course.
    Understand in general how the integration of memory devices into neural network circuits can be realized and the benefits and limitations of these approaches.
    Understand the structure and function of the basic types of artificial neural networks.
    Explain in detail how to perform training of a neural network with the help of backpropagation and the gradient descent algorithm.
Competences and Skills
  • For a passing grade the doctoral student must
  • perform measurement and analysis of the current-voltage characteristics of a RRAM device.
    from a measured polarization-field diagram be able to extract important parameters for a ferroelectric capacitor.
    be able to give suggestions on how speed, reliability and energy consumption can be improved in the memory device technologies treated in the course.
    design and train a neural network to perform image recognition.
Judgement and Approach
  • For a passing grade the doctoral student must
  • ealize the need for energy efficient and scalable neuromorphic hardware for machine learning and AI.
    Evaluate the applicability of a given memory technology for a range of application areas with respect to the pros and cons of the technology.
Types of Instruction
  • Lectures
  • Seminars
  • Laboratory exercises
  • Project
Examination Formats
  • Written exam
  • Written report
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
  • Basic knowledge of device physics
Selection Criteria
  • Chen, A., Hutchby, J., Zhirnov, V. & Bourianoff, G.: Emerging Nanoelectronic Devices. John Wiley & Sons, 2015. ISBN 9781118447741.
Further Information
  • Course Coordinator: Mattias Borg,
Course code
  • EITP25F
Administrative Information
  • 2020-10-01
  • Professor Thomas Johansson

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