Romão Kowaltschuk 1,2 Wilson Arnaldo Artuzi Jr. 1 Oscar da Costa Gouveia Filho UFPR – Universidade Federal do Paraná 2 - Copel – Companhia Paranaense de Energia Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural 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 from a Database Obtained by Electromagnetic Simulation and Neural Networks 1INTRODUCTIONINTRODUCTION 2INDUCTORS DESIGNINDUCTORS DESIGN 3ELECTROMAGNETIC SIMULATIONELECTROMAGNETIC SIMULATION 4RESULTS OF ELECTROMAGNETIC SIMULATIONRESULTS OF ELECTROMAGNETIC SIMULATION 5NEURAL NETWORKSNEURAL NETWORKS 6CONCLUSIONSCONCLUSIONS OUTLINE

- Objective: Transceptor complete integration. - A problem:Passive devices (inductors) integration. Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks TechnologyAdvantagesNegative Points GaAs - Speed - Highly resistive substrate - Passive component construction is not difficult - Low density of integration - High cost Bipolar - 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 from a Database Obtained by Electromagnetic Simulation and Neural 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

C ox = oxide capacitance R Si = silicon conductivity C Si = high frequency capacitive effects that occur in the semicondutor Lumped Parameters Considering the Substrate Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks Basic Electrical Model of the Inductor C S = capacitance of overlaying metal layers R s = conductivity of spiral metal L s = 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 from a Database Obtained by Electromagnetic Simulation and Neural 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 from a Database Obtained by Electromagnetic Simulation and Neural 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 from a Database Obtained by Electromagnetic Simulation and Neural Networks Geometric Specification of InductorsResults of Electromagnetic Simulation Typical Device Specified in Database Database Description

Results of Electromagnetic Simulation Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks

Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks

Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks

Database VariableSearch 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) Resonant Frequency (GHz) , ,132 5, ,104 6, ,796 7, ,580 Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural 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 from a Database Obtained by Electromagnetic Simulation and Neural 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 from a Database Obtained by Electromagnetic Simulation and Neural Networks Results Obtained Using Neural Networks

Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural 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 from a Database Obtained by Electromagnetic Simulation and Neural 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). 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. 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 from a Database Obtained by Electromagnetic Simulation and Neural Networks