NeuroModeler 1.5
Build Neural Models for RF/microwave Design


Artificial Neural Networks are emerging as a powerful technology for RF and microwave characterization, modeling, and design. NeuroModeler is the first software in the industry that embraces this technology with complete RF and microwave orientation. It features fully integrated RF and microwave knowledge based neural network architectures. It helps you to immediately start developing neural models for RF/Microwave components and circuits and helps to provide neural models for your simulators.


Recently, artificial neural networks are introduced to the microwave community, opening the door for an unconventional approach to microwave computer-aided design. Here is a brief outline of the what's, why's, when's and how's, intended to give the RF/microwave engineers the shortest cut to gain from this new technology.

Training of a FET Model

Training Window of a FET Model, with main menu on background

What: Artificial Neural Networks are information processing systems inspired by the ability of human brain to learn from observations and to generalize by abstraction. The fact that neural networks can learn totally different things led to their use in diverse fields such as pattern recognition, speech processing, control, medical applications and more. Recently, microwave researchers developed techniques in which neural networks are trained from microwave data, and then used to enhance microwave design.

Why: The microwave industry's drive for manufacturability-driven design and time-to-market, demands efficient and reliable CAD tools. However, manufacturability-driven design, e.g., statistical design with accurate models such as EM models are time-consuming. Simpler models are fast but are often under limited assumptions or mismatch may occur between computer solutions and hardware measurements. Accuracy, speed and flexibility have been for most of the time contradictory, until recently when neural models for microwave components are introduced. Neural models can be much faster than original detailed models, more accurate and flexible than empirical models, and easier to develop when a new device/new technology is introduced.

When: Neural-based modeling is a generic technique, and shines especially in challenging microwave design situations where conventional techniques balk. Examples are, EM-level modeling but not the expensive EM computations, modeling new components when component formulae are not available, supply user-defined components to simulators when simulator does not easily support user-plugs, etc.

How: NeuroModeler is a RF/microwave oriented software tool, to help you to quickly develop neural models for active and passive components; at both device and circuit-levels; and for linear or nonlinear simulations. The software is written for typical RF/Microwave designers who are not neural network experts but would like to immediately get started with this new technology.

What's Special: NeuroModeler is the first and only software that allows your RF/microwave knowledge to be integrated with neural network learning. You can supply your knowledge through symbolic expressions, or through a circuit netlist with our build-in circuit simulator, or through your own simulator which NeuroModeler can drive, or through a C program source code to be linked with NeuroModeler.

Microstripline Model in Visual Editor

A Microstripline Neural Model defined by Visual Editor

Benefit from an Emerging Technology Right Away: NeuroModeler

 Technical Summary:

Neural model contains a set of neurons and connections between neurons. Each neuron has an activation function processing the incoming information from other neurons. Take a neural model for microwave transmission line as an example, the transmission line geometrical parameters (say x) will be model inputs sent to some neurons called input neurons. After internal processing of all neurons, the neural model will produce electrical quantities (say y) of the transmission line at some neurons called output neurons. In model development stage, samples of x-y data are generated (e.g., from EM simulation or measurement). The model is then trained to learn from the data. Training is similar to an optimization process where internal parameters of the neural model are adjusted such that modeled solution best fits training data. A trained neural model can then be used online during microwave design stage providing fast model evaluation replacing original slow EM simulators. Since neural model is trained directly from data, the model can be developed even if original problem formulae do not exist.

 Highlights of Product Features:

  • Model Creation and Editing: Use NeuroModeler to easily create a neural network model. The built-in default automatically defines a model structure and number of neurons for you. You can choose from a variety of templates or define customer structures of neural models. Attractive visual editor let you define model structures graphically.

  • Data Processing: Here you dictate what the neural network should learn from your data. NeuroModeler automatically performs basic data preprocessing for you. NeuroModeler can even help you to generate data using its Simulation Driver feature.

  • Training: NeuroModeler automatically checks your data and suggests a training technique. The program also has an Auto-Pilot training method, which intelligently adjusts the neural model size and training methods to achieve the required model accuracy. User can modify any training defaults and suggestions.

  • Test: The performance of neural model can be verified using an independent set of data, either with a simple error criteria, or by a variety of detailed plots. You can also evaluate interpolation and extrapolation capabilities of your model.

  • Export: You and your work are not locked to NeuroModeler format. You can export the trained neural model to your own user-environment, be it a spreadsheet, or a computer-source code, or a simulator.

Multilayer Perceptrons, Radial Basis Functions, Wavelet Networks, Knowledge-Based Neural Nets, Space Mapped Nerral Nets, Prior Knowledge Input Networks, Hierarchical Neural Nets, User-defined Structures
Sigmoid, arctangent, hyperbolic tangent, Gaussian, linear, quadratic, polynomial, rational, log, exponential, normalized, multisigmoid, time or frequency domain approximations, inductance/capacitance functions, symbolic expressions, internal or external simulators, user-defined functions.
Adaptive Back-propagation, Sparse optimization, Simplex, Conjugate Gradient, Quasi-Newton, Huber-Quasi-Newton, Auto-Pilot, Genetic Algorithm, Simulated Annealing

 Relevance to the RF/MicrowaveCommunity:

One of the keys to achieve manufacturability-driven design and time-to-market is to use efficient and reliable CAD tools. Components, circuits and systems must be represented by models, as a pre-requisite of any computer-aided design. The availability, accuracy, flexibility of models, and the speed of model computation affect largely the effectiveness at all stages of computer-aided design.

NeuroModeler: Build and use neural network models for RF and microwave modeling, simulation and optimization, an unconventional technology, with surprising answers to some of the toughest problems in RF and microwave computer-aided engineering. Get speed AND accuracy AND flexibility of neural model. Pursue re-usability of the modeling technology for today's AND tomorrow's devices and circuits.

Use NeuroModeler to finish your design sooner than otherwise, to let you focus on immediately getting a model rather than learning component theory, to let you start working with any model you need rather than having to wait and beg for model availability from tools/vendors/manufacturers.

Use NeuroModeler to enhance the "bottom line" (i.e., models) of all CAD tools to help reducing microwave design cycle and time-to-market.

Developed by one of the world's leaders in this new technology, NeuroModeler is the first and a unique software tool of its kind for RF and microwave designers. With NeuroModeler, you can uncover the myth, and bypass the hardships that a non-neural net expert typically encounters when attempting this new technology. Neural Model Technology has never been so reachable, and so connected to RF/microwave designers until today.

For further information on NeuroModeler and NeuroADS, please contact: Professor Q.J. Zhang
Department of Electronics, Carleton University, Ottawa, Ontario, Canada K1S 5B6
email: Web: