Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Built-In Self-Test (BIST): Techniques and Implementations

In course

EE-530: Test of VLSI systems

Test of VLSI Systems covers theoretical knowledge related to the major algorithms used in VLSI test, and design for test techniques. Basic knowledge related to computer-aided design for test techniques, and their integration into a design-flow are presente ...

Description

This lecture covers the concept of Built-In Self-Test (BIST) in VLSI systems, focusing on techniques and circuit configurations that enable a circuit to test itself. It discusses the benefits of BIST, such as improved controllability and observability, reduced reliance on external test equipment, and concurrent testing during normal system operation. The drawbacks and costs of BIST, including area overhead, timing issues, and development challenges, are also explored. The lecture delves into the detailed implementation of BIST, including test result analysis, test pattern generation, and the role of various components like Test Pattern Generator (TPG) and Response Analyzer (RA). Additionally, it explains the use of Linear Feedback Shift Registers (LFSRs) for test pattern generation and the importance of primitive polynomials in generating pseudo-random sequences.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructor

Alexandre Schmid

Alexandre Schmid received the M.Sc. degree in microengineering and the Ph.D. degree in electrical engineering from the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, in 1994 and 2000, respectively. Since 1994, he has been with the EPFL, working with the Integrated Systems Laboratory as a Research and Teaching Assistant, and with the Electronics Laboratories as a Postdoctoral Fellow. In 2002, he was a Senior Research Associate with the Microelectronic Systems Laboratory, where he has been conducting research in the fields of bioelectronic interfaces and implantable biomedical electronics, nonconventional signal processing and neuromorphic hardware, and reliability of nanoelectronic devices, and also teaches with the Microengineering and Electrical Engineering Departments of EPFL. Since 2011, he is a Maître d'Enseignement et de Recherche (MER) Faculty Member with EPFL. He is a coauthor of two books, Reliability of Nanoscale Circuits and Systems, Methodologies and Circuit Architectures, Springer, 2011, and Wireless Cortical Implantable Systems, Springer, 2013, and a coeditor of one book, as well as over 100 articles published in journals and conferences. Dr. Schmid has served as the General Chair of the Fourth International Conference on Nano-Networks in 2009 and has been serving as an Associate Editor of the Institute of Electrical, Information, and Communication Engineers Electronics Express since 2009.

Official source

Related concepts (32)

Linear-feedback shift register

In computing, a linear-feedback shift register (LFSR) is a shift register whose input bit is a linear function of its previous state. The most commonly used linear function of single bits is exclusive-or (XOR). Thus, an LFSR is most often a shift register whose input bit is driven by the XOR of some bits of the overall shift register value. The initial value of the LFSR is called the seed, and because the operation of the register is deterministic, the stream of values produced by the register is completely determined by its current (or previous) state.

Factorization of polynomials

In mathematics and computer algebra, factorization of polynomials or polynomial factorization expresses a polynomial with coefficients in a given field or in the integers as the product of irreducible factors with coefficients in the same domain. Polynomial factorization is one of the fundamental components of computer algebra systems. The first polynomial factorization algorithm was published by Theodor von Schubert in 1793. Leopold Kronecker rediscovered Schubert's algorithm in 1882 and extended it to multivariate polynomials and coefficients in an algebraic extension.

Controllability

Controllability is an important property of a control system and plays a crucial role in many control problems, such as stabilization of unstable systems by feedback, or optimal control. Controllability and observability are dual aspects of the same problem. Roughly, the concept of controllability denotes the ability to move a system around in its entire configuration space using only certain admissible manipulations. The exact definition varies slightly within the framework or the type of models applied.

Primitive part and content

In algebra, the content of a nonzero polynomial with integer coefficients (or, more generally, with coefficients in a unique factorization domain) is the greatest common divisor of its coefficients. The primitive part of such a polynomial is the quotient of the polynomial by its content. Thus a polynomial is the product of its primitive part and its content, and this factorization is unique up to the multiplication of the content by a unit of the ring of the coefficients (and the multiplication of the primitive part by the inverse of the unit).

Cyclotomic polynomial

In mathematics, the nth cyclotomic polynomial, for any positive integer n, is the unique irreducible polynomial with integer coefficients that is a divisor of and is not a divisor of for any k < n. Its roots are all nth primitive roots of unity where k runs over the positive integers not greater than n and coprime to n (and i is the imaginary unit). In other words, the nth cyclotomic polynomial is equal to It may also be defined as the monic polynomial with integer coefficients that is the minimal polynomial over the field of the rational numbers of any primitive nth-root of unity ( is an example of such a root).

Related lectures (32)

State-Space Representation: Structure Theorem

Covers the structure theorem for state-space representations and companion forms.

State-Space Representation: Controllability and Observability

Explores state-space representation, controllability, observability, and regulator calculation using the Ackermann method.

State-Space Representation: Basics & Transformations

Covers the basics of state-space representation and explores transformations to different forms.

Controllability and Observability

Covers controllability and observability in linear systems, discussing necessary conditions and implications of unimodular matrices.

Observability and Controllability