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Aiora Artemis is an AI-assisted optimizer software designed to enhance existing RF EDA tools through boosting the optimization process for greater design accuracy and accelerated computation speed.


First in a series of software utilizing the Aiora AI engine (patent filed), Aiora Artemis is capable of achieving an improvement in design accuracy up to 60% and reducing iterations needed up to 5x. Achieve breakthrough in optimizing complex design with lower risk of premature convergence.


Optimize beyond limits today with Aiora Artemis.

What is Aiora?

How it Works

Aiora Artemis is compatible with Sonnet Suite 19, Ansys HFSS, Mician µWizard with more coming.


Aiora Artemis takes up the role of the optimizer to guide existing simulators in the EDA tools to better perform the optimization process. Preserve all the strength of existing EDA tools and augment it with Aiora Artemis optimizer's strength.

FILPAL is a channel partner of Sonnet Software

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AI Technology

Surrogate Modelling uses Extreme Gradient Boosting (XgBoost) method to approximate complex and computationally expensive functions with simpler mathematical models. This would enable faster evaluation and optimization.

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Multi-Agent reinforcement learning (MARL) is an extension of traditional reinforcement learning. Multiple agents interact and learning simultaneously in an environment. In Aiora Artemis' MARL, each agent consider the presence and actions of other agents in making decision, guiding the solution to optimality.

Multi-Objectives optimizes for multiple criteria such as S-parameters and Radiation Pattern simultaneously through finding a Pareto optimal solution. In Aiora Artemis, multi-objectives allows optimizing for a more robust solution closer to real-world scenario.


Aiora Artemis Performance

Benchmark of Genetic Algorithm (GA), Particle Swarm Optimization (PSO) vs Aiora. Evident improvement in optimization speed of up to 10x and more. The lower the cost, the better the performance

Benchmarking Aiora with conventional optimization on a Extracted Pole Unit (EPU) filter. Aiora can reduce the cost to absolute zero (highest fitness) within a considerably shorter number of evaluations (iterations)

Optimize Better

Aiora Artemis features a curve-mapping goal setting that allow accurate curve mapping for any responses and graph to produce a desired goal response with the highest level of agreement.

Design of Experiments (DOE) reporting like response surface and sensitivity analysis allow users to visualize how the AI model is able to converge on the desired goals and to make informed decisions better. Allowing users to recognize which design parameters is most influential on the optimization.

Contact Us

Live demo available on request


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