Surrogate modeling of RF circuit blocks

Surrogate models are a cost-effective replacement for expensive computer simulations in design space exploration. Literature has already demonstrated the feasibility of accurate surrogate models for single radio frequency (RF) and microwave devices. Within the European Marie Curie project O-MOORE-NICE! (Operational Model Order Reduction for Nanoscale IC Electronics) we aim to investigate the feasibility of the surrogate modeling approach for entire RF circuit blocks. This paper presents an overview about the surrogate model type selection problem for low noise amplifier modeling

A general weak nonlinearity model for LNAs

This paper presents a general weak nonlinearity model that can be used to model, analyze and describe the distortion behavior of various low noise amplifier topologies in both narrowband and wideband applications. Represented by compact closed-form expressions our model can be easily utilized by both circuit designers and LNA design automation algorithms.\ud Simulations for three LNA topologies at different operating conditions show that the model describes IM components with an error lower than 0.1% and a one order of magnitude faster response time. The model also indicates that for narrowband IM2@w1-w2 all the nonlinear capacitances can be neglected while for narrowband IM3 the nonlinear capacitances at the drainterminal can be neglected

RF Circuit linearity optimization using a general weak nonlinearity model

This paper focuses on optimizing the linearity in known RF circuits, by exploring the circuit design space that is usually available in today’s deep submicron CMOS technologies. Instead of using brute force numerical optimizers we apply a generalized weak nonlinearity model that only involves AC transfer functions to derive simple equations for obtaining design insights. The generalized weak nonlinearity model is applied to three known RF circuits: a cascode common source amplifier, a common gate LNA and a CMOS attenuator. It is shown that in deep submicron CMOS technologies the cascode transistor in both the common source amplifier and in the common gate amplifier significantly contributes IM3 distortion. Some design insights are presented for reducing the cascode transistor related distortion, among which moderate inversion biasing that improves IIP3 by 10 dB up to 5 GHz in a 90 nm CMOS process. For the attenuator, a wideband IM3 cancellation technique is introduced and demonstrated using simulations

Developing Visual-Assisted Decision Support Systems across Diverse Agricultural Use Cases

Decision support systems (DSSs) in agriculture are becoming increasingly popular, and have begun adopting visualisations to facilitate insights into complex data. However, DSSs for agriculture are often designed as standalone applications, which limits their flexibility and portability. They also rarely provide interactivity, visualise uncertainty and are evaluated with end-users. To address these gaps, we developed six web-based visual-assisted DSSs for various agricultural use cases, including biological efficacy correlation analysis, water stress and irrigation requirement analysis, product price prediction, etc. We then evaluated our DSSs with domain experts, focusing on usability, workload, acceptance and trust. Results showed that our systems were easy to use and understand, and participants perceived them as highly performant, even though they required a slightly high mental demand, temporal demand and effort. We also published the source code of our proposed systems so that they can be re-used or adapted by the agricultural community

Automatic model type selection with heterogeneous evolution: an application to RF circuit block modeling

Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a cost effective alternative. However, regardless of Moore's law, performing high fidelity simulations still requires a great investment of time and money. Surrogate modeling (metamodeling) has become indispensable as an alternative solution for relieving this burden. Many surrogate model types exist (Support Vector Machines, Kriging, RBF models, Neural Networks, ...) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. The same is true for setting the surrogate model parameters (Bias Variance trade-off). Traditionally, the solution to both problems has been a pragmatic one, guided by intuition, prior experience or simply available software packages. In this paper we present a more founded approach to these problems. We describe an adaptive surrogate modeling environment, driven by speciated evolution, to automatically determine the optimal model type and complexity. Its utility and performance is presented on a case study from electronics

Tailoring Gamification for Adolescents: a Validation Study of Big Five and Hexad in Dutch

status: accepte