ELEC6203 Introduction to MEMS

Module Overview

The student will gain a basic understanding of current MicroElectroMechanical Systems (MEMS) technology and industrial instrumentation systems, with particular emphasis on smart sensors and actuators. The course introduces the fundamental of measurement systems and focuses in particular on MEMS fabrication, MEMS transducer types and applications. This is a core module for the MSc MEMS course and is optional for MSc Bionanotechnology, MSc Nanoelectronics and Nanotechnology, and Pt 4 MEng Electronics and Electromechanical courses.

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

The basic principles of measurement systems
The principles of some common transducer types, their strengths and weaknesses and their use in MEMS
The benefits of MEMS technology in relation to applications

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

Appreciate the scaling effects arising from miniaturising systems
Design a MEMS pressure sensor

Transferable and Generic

Having successfully completed this module, you will be able to:

Structure and write a technical report

Subject Specific Practical

Having successfully completed this module, you will be able to:

Simulate MEMS structures using finite element analysis

Syllabus

Introduction Lecture
Microfabrication
Magnetic Sensors
Piezoresistive MEMS
Pressure Sensors
Thick-film and PiezoMEMS
MEMS Actuators
Resonant sensors
MEMS resonant sensors
Measurement systems
Physical sensors
Modelling the dynamics of sensor systems
Intelligent sensors
Thermal sensors
Charge amplifiers

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture		36
Tutorial		12
Computer Lab	COMSOL	12

Assessment

Assessment methods

Laboratory sessions are scheduled in the labs on level 2 of the Zepler building
Length of each session: 3 hours
Number of sessions completed by each student: 3
Max number of students per session: unlimited
Demonstrator:student ratio: 1:12
Preferred teaching weeks: 6 to 11

Method	Hours	Percentage contribution
laboratory report	-	30%
Exam	2 hours	70%

Referral Method: By examination

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ELEC6202 Advanced Memory and Storage

Module Overview

The aim of this module to provide an overview of advancement of memory and storage devices in line with the development of nanoelectronics and nanotechnology. Students will gain knowledge of how silicon device scaling has moved semiconductor memory into the nanoelectronics area. Then they will become familiar with state-of-the-art non-volatile memory technologies which are intended to replace or complement semiconductor memory. Longer term data storage in the form of high density portable devices such as hard disks and Blu Rays will be discussed as well.

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

device scaling of semiconductor memory
the underlying operating principles of state-of-the-art nanodevices for memory and storage applications
practical characterisation of materials used for nanodevices

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

demonstrate specialised practical and theoretical knowledge of particular nanodevices for memory applications

Transferable and Generic

Having successfully completed this module, you will be able to:

ability to write a short essay on a given subbject using knowledge from hournal articles and lectures
understand the inter-relation between different technologies in the design of integrated devices

Syllabus

Semiconductor Memory

Static Random Access Memory
Dynamic Random Access Memory
Flash (3D integration)

Advanced Nonvolatile Memory

Ferro-electric RAM
Phase Change RAM
Resistive RAM
Magnetic RAM
Emerging memory technology

Data storage

Hard Disk Drives
DVD and Blu Ray

Laboratory

Raman characterisation of device materials

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture	Lectures on the module topics	20
Tutorial	Coursework feedback and exam revision	3
Specialist Lab	Raman characterisation laboratory, tutorials and laboratory sessions	6

Assessment

Assessment methods

The lab report will not be marked if the student has not attended the Raman characterisation lab sessions

Method	Hours	Percentage contribution
Advanced memory device review (20%) and laboratory report (30%)	-	50%
Exam	2 hours	50%

Referral Method: By examination

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ELEC6201 Microfabrication

Module Overview

This module provides an overview of modern microfabrication technologies, and is as such structured around the state-of-the-art facilities in the new Southampton Nanofabrication Centre. The various fabrication techniques that are relevant for microdevices in the field of electronics, optoelectronics and micro-electro-mechanical-systems (MEMS) will be addressed in the lectures, with an emphasis on their physical and chemical principles. The integration of these techniques will be explained with an example of a complete process flow for the fabrication of a specific microdevice. The organization of a fabrication facility, including risk assessment aspects, will be addressed with a cleanroom tour and laboratory-based coursework. This laboratory will take place in the clean room.

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

The microfabrication technology for the microsystem devices that are used in modern electronic, optoelectronic and lab-on-a-chip applications
Fabrication process flow in a cleanroom environment

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

Appreciate how fabrication process limitations influence device design
Design a process flow for electronic/optoelectronic/MEMS microsystem devices

Transferable and Generic

Having successfully completed this module, you will be able to:

Write concise technical/laboratory reports in the format of a journal paper

Subject Specific Practical

Having successfully completed this module, you will be able to:

Perform some basic microfabrication pattern transfer processes in a cleanroom environment
Perform some device characterization procedures

Syllabus

Microfabrication introduction and overview

Material for fabrication
Fabrication equipment
Growth technology
Silicon-based process

Fabrication technology

Pattern transfer technology

Lithography – photolithography
Resist technology
Micromachining – wet etch and dry etch

Materials processing technology

Material deposition methods
Basic ion implantation and diffusion doping process
Low and high temperature process
Device packaging methods

Characterisation technology for microfabrication process
Design criteria and fabrication method for micro device applications
Microfabrication process integration

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture	24 lecture hours	24
Specialist Lab	Microfabrication cleanroom laboratory	6
Tutorial	Tutorial session	6

Assessment

Assessment methods

The coursework will not be marked if the student has not attended the laboratory sessions.

Method	Hours	Percentage contribution
Fabrication report	-	30%
Exam	2 hours	70%

Referral Method: By examination

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ELEC3210 Design Studies

Module Overview

The design of an artefact is a complex sociotechnical problem. The module is designed to extend the students’ knowledge of the design process beyond normally expected of a graduate engineer (as defined by the accreditation process). The module in part will take a Design Thinking approach, which considers the cognitive processes involved in design.

While the technical problems are widely understood, a holistic view of the design process includes considerations of problems of how does the design team capture the design rationale, what are the risks associated with the design relative to the costs of mitigation, how is the IPR in the additive manufacturing process.

In taking the module student will gain an in depth understanding of design processes and how the sociotechnical issues will impact on the pure technical challenges.

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

understand the design process from the perceptive of various stakeholders
formulate and articulate a design problem correctly.
be capable of making informed design desisions based on known parameters

Transferable and Generic

Having successfully completed this module, you will be able to:

communicate the design solution effecitively.
work as part of a small group or team in a professional manner

Subject Specific Practical

Having successfully completed this module, you will be able to:

use tools to both capture the design rationale and undertake its analysis

Syllabus

What is design. Iconic Designs.
What is design thinking and how does it influence the designer and the design process. The wicked problem
Why does a design fail - materials and processes.
The design process - assessment, problem formulation, abstraction, analysis, implementation.
Design for X and n^thgeneration design.
Design Knowledge. Capture and Reuse; Representation and codification; web based systems. How does a design team operate
Risk, how to measure and avoid. ALARP
Quality- what is quality, how can it be measured. TQM, six sigma, Deming Wheel, Quality Functional Deployment
Additive manufcaturing - the IPR and legal challanges

Learning & Teaching

Learning & teaching methods

The module will be seminar based, with a high level of student participation through the study and presentation of industrial and research case studies.

Activity	Description	Hours
Lecture	The lectures are design to be interactive seminars where student participation in encouraged	24

Assessment

Assessment methods

The design exercise is a group activity in which design rational capture and assessment tools are to be used. Assessment requires the production of a group report, presentation and individual refection documents.

Method	Hours	Percentage contribution
Design Thinking Exercise	-	50%
Design Study: preparing a design to resolve a specific issue	-	50%

Referral Method: By set coursework assignment(s)

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ELEC3208 Analogue and Mixed Signal Electronics

Module Overview

To cover in some depth those areas of circuitry likely to be used between an analogue signal source and a digital signal processing system, making maximum use of available integrated circuits.

This fits in with our overall programme of providing a broad based electronics engineering course, with this module covering the main aspects of measuring outputs from a variety of sensors, designing interface circuits and amplifiers, filtering, and data conversion.

The course will also cover important topics such as clock generation, noise management, power supply design, as well as practical issues such as packaging, EMC and PCB design.

It is assumed that the students at least understand the basics of opamp circuits, and basic analogue to digital conversion principles as for example covered in part 1 ELEC1207 and part 2 ELEC2216 Advanced Electronic Systems.

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Understand the various techniques which can be used for signal conditioning, including filter design
Understand the sources of noise in electronic circuits, and the limitations they impose
Understand the various techniques used for analogue to digital conversion and their relative merits

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

design Interface and Amplifier circuits

Syllabus

Signal conditioning using op amps and discrete devices

Review of available op amp specifications
Interface requirements for various signal sources (voltage, current, charge)

Analogue filters

Filter types, filter implementation – passive, active, IIR FIR
Filter approximations, max flat amplitude, ripple, transmission zeros, group delay; Butterworth, Chebyshev, Bessel, Elliptic
Filter transformations, normalised filter design methods
Passive filter implementation, terminated filters
Active filter types; gyrator, single op-amp S-K, Rausch
State-variable Tow-Thomas Biquad circuit, derivation
Biquad tuning, LP/BP/BS/AP/Equaliser variants
Biquad Q tuning and use as a sine wave oscillator
Practical implementation issues; dynamic range, component sensitivity; adaptive tuning

Introduction to phase locked loops

Basic operation of PLL, linear phase domain model
Oscillators and basic phase detectors
Tracking filter and FM demodulator
Advanced phase detectors;
Clock and data recovery

Analogue to digital conversion

ADC specs, linearity, resolution, linearity, bandwidth (revision)

Fundamentals of noise in digital and analogue systems
Sample and Hold Circuits, track and hold
Quantising noise, anti-aliasing
More ADC types ; flash, dual slope

Transmission lines for HF signals

Basic theory; coax, parallel wire, stripline
Characteristic impedance, termination
Pulse behavior with mismatch
Frequency dependent characteristics
Applications issues in data comms and RF; losses; intersymbol interference; SWR

Digital to Analogue Conversion

R-2R Digital to Analogue Converters
PWM Signal Generation
Applications of digital to analogue converters

Low noise amplifiers

Fundamentals
Physical noise models, passive and active devices
Input referred noise model
Noise in simple amplifiers and opamps
Discrete and op-amp specs
Practical low noise input circuitry

Power Supplies and Power Amplifiers

Linear Regulators
Buck Converters
Boost Converters
Power Supply Stability
Power Amplifiers

Class A/AB/B/D Amplifiers
Applications (Audio and Power)

Driver Circuits

Power Conversion

AC/DC Rectification
Inverter Design

Practical Aspects

EMC
PCB Design
Screening
Thermal Design Aspects

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture		36
Tutorial		12

Assessment

Assessment methods

Method	Hours	Percentage contribution
Analogue Circuit Design	-	10%
Exam	2 hours	90%

Referral Method: By examination

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COMP3211 Advanced Databases

Module Overview

This module builds on the first year Data Management module by examining the construction of database management systems, and the data structures and algorithmic techniques used to represent and manipulate data effectively.

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

The internals of a database management system
The issues involved in developing database management software
The variety of available DBMS types and the circumstances in which they're appropriate

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

Choose appropriate approaches for data storage and access
Demonstrate how a DBMS processes, optimises and executes a query
Identify issues arising from concurrent or distributed processing and select appropriate approaches to mitigate those isues

Subject Specific Practical

Having successfully completed this module, you will be able to:

Select an appropriate DBMS for an application
Implement components of a DBMS

Syllabus

DBMS Internals
Relational Algebra
Data

Types of data, including spatial and temporal

Data Storage

The memory hierarchy
Fields, records and blocks
The Five Minute Rule
Row stores vs. column stores

Access Structures

Indexes
B-Trees
Hash tables
Multidimensional Access Structures: grid file, partitioned hash, kd-tree, quad-tree, R-tree, UB-tree, bitmap indexes

Query Processing

Physical plan operators: one-pass algorithms, nested-loop joins, two-pass algorithms
Query optimisation: algebraic laws, cost estimation, cost-based plan selection

Transaction Processing: chained transactions, nested transactions, savepoints, compensating transactions
Concurrency
Parallel Databases

Partitioning techniques
Types of parallelism: intraquery, interquery, intraoperation, interoperation

Distributed Databases
Message Queues
Stream Processing
Information Retrieval
Data Warehouses and OLAP
Non-Relational Databases: hierarchical, network, object-oriented, object-relational, XML
NoSQL

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture		36

Assessment

Assessment methods

Method	Hours	Percentage contribution
Database Programming Exercise	-	25%
Exam	2 hours	75%

Referral Method: By examination

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COMP3210 Advanced Computer Networks

Module Overview

This module is designed to be a follow-up module to the Computer Science or ITO second year introductory networking module.

The module is split fairly evenly between two areas, each principlally delivered by a separate lecturer. The wireless part reviews wireless technologies and their application in areas such as sensor networking. The coursework gives students the opportunity to get hands-on experience with wireless networking for small, embedded devices. The other part of the module reviews emerging networking technologies, which might include topics such as future routing protocols, IPv6 transition, and software-defined networking.

Students should consider taking this module if they are interested in learning about networking systems and their architectures in more detail than covered in the introductory module, and exploring emerging topics which are delivered as part of ECS' research-led teaching philosophy.

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Operation of wireless networks
Emerging topics in computer networks
A range of network architectures and protocols

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

Design and critically analyse networking protocols for a range of technologies and scenarios

Subject Specific Practical

Having successfully completed this module, you will be able to:

Build a wireless networking demonstrator for an embedded system

Syllabus

Wireless networking:

Bluetooth, 802.11 standards
Information theory, bandwidth, multiple access
Wireless sensor networks
Wireless roaming; eduroam
Mobile IPv6; host and network mobility

Emerging networking technologies:

Host configuration and service discovery principles
Future routing architectures
IPv6 deployment scenarios and challenges, IPv6 transition/integration
Advanced IP multicast, including IPv6 multicast and SSM
Software-defined networking
Delay-tolerant networking
Future home network architectures
IP network management and monitoring
Network security; intrusion detection and prevention

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture	Regular lecture slots.	21
Tutorial	Tutorial support on the coursework and specific lecture topics	8
Seminar	Guest lectures from industry experts	4
Tutorial	Revision (last teaching week)	3

Assessment

Assessment methods

Method	Hours	Percentage contribution
Wireless networking for embedded systems	-	30%
Exam	2 hours	70%

Referral Method: By examination

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COMP3208 Social Computing

Module Overview

The aim of this module is to introduce the fundamental concepts and computational techniques used in social computing. In a broad sense, social computing includes all situations where social interactions are supported by computers. More specifically, in this module we will focus on three main areas: crowdsourcing, online auctions (including online advertising), and recommender systems. The module has a large practical component where you will learn how to solve a problem using crowdsourcing, and you will learn how to set up such experiments and dealing with incentives. In addition, you will learn about technologies such as auctions and algorithms for recommender systems. Note that this module does not cover social networking as this is covered elsewhere.

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

concepts and example applications from social computing, including crowdsourcing, recommender systems, and online auctions
incentives in crowdsourcing applications
applications in crowdsourcing
the auctions used in online advertising

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

use recommender technologies such as item-based and user-based collaborative filtering techniques
describe the most important techniques and issues in designing, building and modelling social computing systems

Subject Specific Practical

Having successfully completed this module, you will be able to:

set up social computing experiments and analyse the results using a scientific approach

Syllabus

Crowdsourcing

Human computation
Participatory sensing
Citizen science
Amazon Mechanical Turk and other platforms

Web analytics and Experimental design

A/B split testing
Latin squares

Incentives and monetary payments in crowdsourcing
Prediction markets
Reputation systems

User-based collaborative filtering
Item-based collaborative filtering

Online auctions

Sponsored search
Display advertising

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture		36

Assessment

Assessment methods

Method	Hours	Percentage contribution
implementation and analysis of social computing system	-	30%
Exam	2 hours	70%

Referral Method: By examination

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COMP3207 Cloud Application Development

Module Overview

During the first two years of the degree students gain experience in a variety of 'traditional' programming languages in procedural, functional and object-oriented flavours. This module addresses the design and use of scripting languages for a contemporary cloud-based computing application.

Explore the role of scripting languages in cloud applications
Introduce Python and Javascript and their applications
Provide experience in cloud computing
Provide an appreciation of new concepts in a rapidly developing field

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

The role of scripting languages
The syntax and semantics of languages such as Python and JavaScript
Cloud computing and its advantages and disadvantages

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

Compare and contrast the features and capabilities of scripting languages used for cloud computing applications
Compare and contrast scripting languages with other programming languages
Select an appropriate scripting language for the development of a given cloud-based application
Read programs in a range of scripting languages

Subject Specific Practical

Having successfully completed this module, you will be able to:

Design and implement a cloud-based application

Syllabus

Cloud computing

introduction and examples
advantages and disadvantages
taxonomy of cloud computing: PaaS, SaaS, IaaS

Python

overview, introduction and examples
advantages and disadvantages
Google App Engine

JavaScript

client-side web scripting: DOM and AJAX
server-side applications: node.js

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture		36

Assessment

Assessment methods

Method	Hours	Percentage contribution
Individual Assignment	-	40%
Group Assignment	-	60%

Referral Method: By set coursework assignment(s)

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COMP3206 Machine Learning

Module Overview

This module aims to introduce the mathematical foundations for machine learning and a set of representative approaches to address data-driven problem solving in computer science and artificial intelligence.

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Underlying mathematical principles from probability, linear algebra and optimisation
The relationship between machine learning and neurophysiology

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

Characterise data in terms of explanatory models
Use data to reinforce one/few among many competing explanatory hypotheses

Subject Specific Practical

Having successfully completed this module, you will be able to:

Systematically work with data to learn new patterns or concepts
Gain facility in working with algorithms to handle data sets in a scientific computing environment

Syllabus

Historical Perspective

Biological motivations: the McCulloch and Pitts neuron, Hebbian learning.
Statistical motivations

Theory

Generalisation: What is learning?
The power of machine learning methods: what is a learning algorithm? what can they do?

Probability

Probability as representation of uncertainty in models and data
Bayes Theorem and its applications
Law of large numbers and the Gaussian distribution
Markov and graphical models

Supervised Learning

Classification using Bayesian principles
Perceptron Learning
Support Vector Machines and Kernel methods
Neural networks/multi-layer perceptrons (MLP)
Features and discriminant analysis

Linear Algebra

Using matrices to find solutions of linear equations
Properties of matrices and vector spaces
Eigenvalues, eigenvectors and singular value decomposition

Data handling and unsupervised learning

Principal Components Analysis (PCA)
Blind source separation using Independent Components Analysis (ICA)
K-Means clustering
Spectral clustering
Manifold learning

Regression and Model-fitting Techniques

Linear regression
Polynomial Fitting
Kernel Based Networks

Optimisation

Convexity
1-D minimisation
Gradient methods in higher dimensions
Constrained optimisation
Dynamic Programming

Case Studies

Example applications: Speech, Vision, Natural Language, Bioinformatics.

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture	Lectures using whiteboard and slides	20
Computer Lab	Timetables computer lab sessions during weeks 8 and 9	6

Assessment

Assessment methods

The coursework items will be varied in scope and will require different degrees of effort. Marks will be distributed accordingly, and the distribution will be made clear to students in advance.

Method	Hours	Percentage contribution
Assignment on implementing machine learning algorithms	-	20%
Exam	2 hours	80%

Referral Method: By examination

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