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This paper presents an innovative cross-coupled differential drive rectifier for dual-band Radio frequency identification (RFID) tags. A self-compensating rectifier with enhanced power-conversion efficiency (PCE) at low and high RF power is proposed. The proposed rectifier utilizes a self-compensating circuit, the extra two cross-couple transistors, to increase the gate voltage of transistors to control the conduction of the rectifying transistors. In order to adapt high frequency (HF) and ultrahigh frequency (UHF) rectification, the matching circuit is designed with parasitic cancellation technology. Moreover, the cascading power management circuits are added to generate a stable output voltage. The multi-standard rectifier is designed and simulated in 0. 1 8 μ m CMOS process. The simulated result shows that the proposed three-stage rectifier achieves a PCE 74% (at 5 0 0 k Ω load) when receiving a 915 MHz signal with average power of − 2 5 dBm. Moreover, the maximum efficiency achieves 56% at HF 13.56 MHz, and the final output voltage can be stabilized at a specific voltage of 1.052 V. [ABSTRACT FROM AUTHOR]

Alternate test can dramatically reduce the cost of circuit testing. In one type of alternate test, copies of parts of the circuit and process-control monitors (PCM) are used as non-intrusive sensors for calibration algorithms, which can be time-consuming. A straightforward design procedure for process-aware sensors is proposed and proved efficient, and calibration results with process-aware sensors of different sizes are compared. Device size variation and process variation were found to have an impact on the mapping accuracy of the model. In addition, the results were found to degrade with a decrease in sensor size. Specifically, the low-noise amplifier (LNA) pass ratios with the largest sensor size were 3% ~ 4.5% less than the ratios obtained without the prediction error of alternate test. When the size of sensors was reduced by up to 30 times, the LNA pass ratio dropped by 5% ~ 7%. [ABSTRACT FROM AUTHOR]

To enhance the stability and robustness of the DC converter, a two-dimensional (2D) iterative learning control method suitable for the Buck converter is proposed. This control method is derived through a continuous-discrete model. Combining the error dynamics equation and the iterative learning method (ILM) described with Roesser theory, effective learning rules and sufficient conditions for convergence are obtained. By contrast with the iterative learning algorithm of the conventional Buck converter, this algorithm clarifies the learning gain, deeply learns the structure and parameters of the converter, and obtains an accurate learning dynamics model. Moreover, numerical simulations of the Buck converter with specific circuit parameters are also conducted. The simulation results demonstrate that the control learning rules are less restrictive with a swift transient response, which are robust without complex feedback compensation circuits. Thereby, it elucidates a new approach to the application of the Buck converter. [ABSTRACT FROM AUTHOR]