Prepared by: Prof. Oleg Wasynczuk

Phone: 494-3475

Email: wasynczu@ecn.purdue.edu (preferred)

Office: EE 144

Basic Course Description

Course Number: ECE-546

Course Title: DIGITAL COMPUTATIONAL TECHNIQUES FOR ELECTRONIC

CIRCUITS

Credit: Class: Lab:

Projected Term(s) Offered: Spring Semesters of even years

Prerequisite(s): ECE-255, ECE-301

Co-requisite(s):

Prerequisites by Topic: Exposure to circuit models of transistors and diodes, ordinary differential and difference equations.

Co-requisites by Topic:

Course Description: Digital computer methods for dc, ac, and transient analyses of electrical and electronic circuits. Linear, nonlinear, and piecewise linear dynamic circuits are considered. Algorithms used in circuit/system simulators such as SPICE, Saber, EMTP, Simulink, and ACSL are studied.

Required Text: Lawrence Pillage, "Electronic Circuit & System Simulation Methods," McGraw Hill, 1995, 0-07-050169-6.

Recommended References:

Chua and Lin, Computer Aided Analusis of Electronic Circuits: Algorithms and Computational Technques, Prentice Hall, 1975, 0-13-165415-2.

Vlach and Singhal, Computer Methods for Circuit Analysis and Design; Van Nostrand Reinhold, 1994. 0-442-01194-6.

M. Crow, Computational Methods for Electric Power Systems, CRC Press, 2003, 0-8493-1352-X.

Assorted Papers

Course Outline

(The course outline should reflect the principal topics covered and the approximate time spent on each topic.—It should not be a day-by-day schedule of lectures. The latter is information best provided in the course syllabus.)

Principal Topics

1	Overview of circuit simulation programs.
5	DC and AC analysis of linear networks. Explicit form of nodal equations. Gaussian elimination and LU factorization.
3	DC analysis of nonliner resistive networks. Fixed-point and Newton- Raphson algorithms. Companion model.
5	Implicit integration. Discretized circuit models for capacitors and inductors. Transient analysis.
3	Circuit models for semiconductor devices (diodes, BJT, FET). Macromodel for op amps.
5	Computer formulation of Kirchoff's laws, fundamental loop, and cutset matrices.
5	Formulation of state space models.
5	Sparse matrix techniques. Fill-ins and ordering algorithms. Packed vector implementation.
6	Numerical integration methods. Stability region of numerical integration algorithms.
3	Computational complexity of nodal and state-model-based solvers.
3	Cosimulation methods.

Course Outcomes: A student who successfully fulfills the course requirements will have demonstrated:

1) Knowledge of fundamental algorithms used in circuit and system simulators [1,2,3;a,e].

2) Ability to select appropriate algorithm/parameters to specific circuits/systems [1,3;a,e].

3) Ability to formulate and implement complex circuit/system models [1,4;a,e].

4) Understanding of stability/convergence properties of numerical integration algorithms [1,2;a,e].

5) Knowledge of cosimlation methods [1,3;a,e].

Outcome Assessment Method: Midterm and Final Exam, Four projects.

Final Grade:

The final grade is determined in accordance with the following:

Average = 0.4*(Exam Scores) +

0.5*(Average Project Score) +

0.1*(Average Homework Score)

Cheating Policy:

At a minimum, cheating will result in a zero on the assignment/project/exam in question. In the case of regular exam, this score cannot be the one dropped. At a maximum, it will result in failure of the course, and possible dismissal from Purdue. All instances of cheating, even suspected cheating, will, without exception, be reported to the Assistant Dean of Students.