227-1032-00L  Neuromorphic Engineering II

SemesterSpring Semester 2019
LecturersT. Delbrück, G. Indiveri, S.‑C. Liu
Periodicityyearly recurring course
Language of instructionEnglish
CommentInformation for UZH students:
Enrolment to this course unit only possible at ETH. No enrolment to module INI405 at UZH.

Please mind the ETH enrolment deadlines for UZH students: Link



Catalogue data

AbstractThis course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the fall semester course "Neuromorphic Engineering I".
ObjectiveDesign of a neuromorphic circuit for implementation with CMOS technology.
ContentThis course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the autumn semester course "Neuromorphic Engineering I".

The principles of CMOS processing technology are presented. Using a set of inexpensive software tools for simulation, layout and verification, suitable for neuromorphic circuits, participants learn to simulate circuits on the transistor level and to make their layouts on the mask level. Important issues in the layout of neuromorphic circuits will be explained and illustrated with examples. In the latter part of the semester students simulate and layout a neuromorphic chip. Schematics of basic building blocks will be provided. The layout will then be fabricated and will be tested by students during the following fall semester.
LiteratureS.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation.
Prerequisites / NoticePrerequisites: Neuromorphic Engineering I strongly recommended

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a two-semester course together with 227-1033-00L Neuromorphic Engineering I
ECTS credits12 credits
ExaminersT. Delbrück, G. Indiveri, S.-C. Liu
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationoral 30 minutes
Performance assessment as a semester course (other programmes)
ECTS credits6 credits
ExaminersT. Delbrück, G. Indiveri, S.-C. Liu
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationoral 20 minutes
Additional information on mode of examinationCompletion of lab exercises and work on class project.
Final grade based on combination of project work (30%) and final oral exam (70%).
In addition, there are 6 exercises and students must do at least 5 of them in order to receive any passing grade.
(compulsory continuous performance assessment)
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main linkInformation
Only public learning materials are listed.

Courses

NumberTitleHoursLecturers
227-1032-00 GNeuromorphic Engineering II
**together with University of Zurich**

Vorlesung: 13-15
Übungen: 15-18
5 hrs
Tue13-15I55 G 20 »
15-18I35 E 30 »
T. Delbrück, G. Indiveri, S.‑C. Liu

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Biomedical Engineering MasterTrack Core CoursesWInformation
DAS in Data ScienceNeural Information ProcessingWInformation
Data Science MasterInterdisciplinary ElectivesWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Electrical Engineering and Information Technology MasterSpecialization CoursesWInformation
Neural Systems and Computation MasterElectivesWInformation
Neural Systems and Computation MasterNeurotechnologies and Neuromorphic EngineeringWInformation
Physics MasterSelection: Neuroinformatics /INIWInformation
Computational Science and Engineering BachelorElectivesWInformation
Computational Science and Engineering MasterElectivesWInformation