Embedded test and control of analogue/RF circuits using intelligent resources

The emergence and the fast development of distributed sensor networks associated with the popularisation of cellular phone handset portable has supported an explosive growth of wireless applications in the portable devices. Consequently radio frequency (RF) system and their increasing functional densities have progressed remarkably. Wireless integrated network sensors combine sensing, signal processing, decision capability, and wireless networking capability in a compact, low power system. Despite the rapid proliferation of sensor network applications and other devices incorporating wireless communication networks, there is no generic method for testing analogue and mixed signal (AMS) blocks (including analogue circuits, MEMS and RF) which are included in these devises. On the other hand, it is obvious that energy efficiency of RF transceivers is critical paramount for longer life in portable devices. Therefore, efficient energy consumption of RF power amplifiers (PAs) and RF low noise amplifiers (LNAs) are key components in wireless mobile battery – operated systems determine the total power consumption since they dominate the power consumption of the other component of the RF transceivers [1]. In this study, we propose a new approach of alternate test and adaptive control of power supply of AMS blocks based on the identification of some parameters of a behavioural model. A complete behavioural model of AMS block is built, including a model structure and a set of parameters. Using a population of circuits obtained by Monte Carlo simulation, a regression relationship is then built to link the system performance to the parameters of its behavioural model. A virtual performance measurement can thus be obtained from the identified parameters of the behavioural model of the current system. The estimated performances are then used either to make the pass / fail decision for test purpose or as virtual measurements for performance control of in order to optimize energy consumption. Figure 1 gives an overview of our approach.