Romão Kowaltschuk1,2

Wilson Arnaldo Artuzi Jr.1

Oscar da Costa Gouveia Filho1

1 - UFPR – Universidade Federal do Paraná

2 - Copel – Companhia Paranaense de Energia

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

September, 2003

Design of Integrated Inductors Through Selection from a Database Created Using Electromagnetic Simulation and Neural

Networks

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

1 INTRODUCTION

2 INDUCTORS DESIGN

3 ELECTROMAGNETIC SIMULATION

4 RESULTS OF ELECTROMAGNETIC SIMULATION

5 NEURAL NETWORKS

6 CONCLUSIONS

OUTLINE

- Objective: Transceptor complete integration.- A problem:Passive devices (inductors) integration.

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

Technology Advantages Negative Points

GaAs

- Speed- Highly resistive substrate- Passive component construction is not difficult

- Low density of integration- High costBipolar

- Speed- High Fan-out avaiability

- Low density of integration

CMOS- Low power- High density of integration- Low cost

Bipolar/CMOS

- Conductive substrate

-Isn’t avaiable in standard manufacturing plants

- Joins advantages of bipolar/CMOS

INTRODUCTION

INDUCTORS DESIGN

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

Inductors

- Devices with no standard design

Project Techniques

- Empirical formulations

- Analythic formulation derived from electromagnetic theory

- Electromagnetic simulation (finite elements and numerical methods)

Design Variables

- Too many variables to be chosen in design

Cox = oxide capacitanceRSi = silicon conductivityCSi = high frequency capacitive effects that occur in the semicondutor

Lumped Parameters Considering the Substrate

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

Basic Electrical Model of the Inductor

CS = capacitance of overlaying metal layersRs = conductivity of spiral metalLs = high frequency inductive effects of that occur in the spiral metal

Lumped Parameters Considering the Spiral

Belief: the electromagnetic simulation gives a good evaluation of results, concerning the variation of reactance with frequency, but it demands a lot of effort!

Solution: to do electromagnetic simulation automatically!

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

Electromagnetic Simulation - An Alternative Solution

An inductor base case editor program for batch simulation

An electromagnetic specific purpose simulator (ASITIC)

capable of providing continous outputs for simulation cases

of thousands of devices.

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

A program to classify results, due to the huge amount of

data!

(a simple software written in VB6)

Automatization of Electromagnetic Simulation

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

Geometric Specification of Inductors Results of Electromagnetic Simulation

Typical Device Specified in Database

Database Description

Results of Electromagnetic Simulation

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

Database Variable Search Criteria

- Normalized inductive reactance between input and output terminals

5,0 nH<=jX/jw<= 5,2nH

- Inductor’s spiral circumscript radius- Resonant Frequency

Radius < 200 m

f > 3,5 GHz

Partial Vision of the Answer to the Requested Question

Spiral Ident.Number

Radius(m)

Operational freq.(MHz)

Normal. Induc.Reactance(nH)

ResonantFrequency(GHz)

931....9311048.....1048

175....175150.....150

200....1600200.....1400

5,094....5,1325,090.....5,104

6,922....8,7967,088.....6,580

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

Design Example

A possible answer: evaluating some values of the electrical

parameters database using neural

networks trained using a smaller set of

data obtained by eletromagnetic

simulation.

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

Question: how could it be possible to decrease the

time spent in eletromagnetic simulation?

Creating the Inductor’s Electrical Database Using Neural Networks

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

Results Obtained Using Neural Networks

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

The proposed design method enables evaluation of inductive

reactances for a wide range of frequencies and can justify the

development of the sofware tools and the avaiability of computer

resources necessary to realize it.

CONCLUSIONS

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks

The evaluation of normalized inductive reactances, through

electromagnetic simulation is the only theoretical model that

shows the designer a trustable performance of inductors, as

frequency varies in a wide range.

The alternative design method of creating some of the values

necessary to complete a searchable database employing neural

networks has achieved reasonable results just for evaluating

reactances of big and medium size inductors (outer sides >= 100

µm).

For smaller devices, the performance of neural networks is not

acceptable. The values obtained are worth just for indicating a

range of values.

CONCLUSIONS

Design of Integrated Inductors through Selection froma Database Obtained by Electromagnetic Simulation andNeural Networks