170 likes | 296 Views
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 Created Using Electromagnetic Simulation and Neural Networks. Romão Kowaltschuk 1,2
E N D
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 Created Using Electromagnetic Simulation and Neural Networks 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 September, 2003
Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks OUTLINE • INTRODUCTION • INDUCTORS DESIGN • ELECTROMAGNETIC SIMULATION • RESULTS OF ELECTROMAGNETIC SIMULATION • NEURAL NETWORKS • CONCLUSIONS
Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks INTRODUCTION - Objective: Transceptor complete integration. - A problem:Passive devices (inductors) integration. Technology Advantages Negative Points • - Speed • - Highly resistive substrate • Passive component construction • is not difficult - Low density of integration - High cost Bipolar GaAs - Speed - High Fan-out avaiability - Low density of integration - Low power - High density of integration - Low cost - Conductive substrate CMOS Bipolar/CMOS -Isn’t avaiable in standard manufacturing plants - Joins advantages of bipolar/CMOS
Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks INDUCTORS DESIGN Design Variables Inductors - Devices with no standard design - Too many variables to be chosen in design Project Techniques - Empirical formulations - Analythic formulation derived from electromagnetic theory - Electromagnetic simulation (finite elements and numerical methods)
Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks Basic Electrical Model of the Inductor Lumped Parameters Considering the Spiral CS = capacitance of overlaying metal layers Rs = conductivity of spiral metal Ls = high frequency inductive effects of that occur in the spiral metal Lumped Parameters Considering the Substrate Cox = oxide capacitance RSi = silicon conductivity CSi = high frequency capacitive effects that occur in the semicondutor
Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks Electromagnetic Simulation - An Alternative Solution 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 Automatization of Electromagnetic Simulation 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. A program to classify results, due to the huge amount of data! (a simple software written in VB6)
Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks Database Description Geometric Specification of Inductors Results of Electromagnetic Simulation Typical Device Specified in Database
Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks 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 Design Example Database Variable Search Criteria - Normalized inductive reactance between input and output terminals 5,0 nH<=jX/jw<= 5,2nH Radius < 200 m - Inductor’s spiral circumscript radius - Resonant Frequency 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) 175 .... 175 150 ..... 150 200 .... 1600 200 ..... 1400 5,094 .... 5,132 5,090 ..... 5,104 6,922 .... 8,796 7,088 ..... 6,580 931 .... 931 1048 ..... 1048
Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks Creating the Inductor’s Electrical Database Using Neural Networks Question: how could it be possible to decrease the time spent in eletromagnetic simulation? 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 Results Obtained Using 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 CONCLUSIONS • 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. • 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.
Design of Integrated Inductors through Selection from a Database Obtained by Electromagnetic Simulation and Neural Networks CONCLUSIONS • 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.