Energy efficiency is of utmost importance in modern applications. Power consumption optimisation could be improved by a comprehensive analytical modeling of the characteristics of critical blocks in a system. Dynamic range (DR) has a strong effect on the power consumption of analog circuits, and is determined by circuit non-linearity and noise level. Noise is well modelled even in deep sub-micron technologies, yet there is a lack of analysis and modeling of the non-linearity. An analytical MOSFET differential pair non-linearity model is presented in this work. The proposed model is universal to a wide range of technologies from long to ultra-deep sub-micron devices, and is valid for all operating regions as it is based on the EKV MOSFET model. Furthermore, a model including drain-voltage-induced non-linearity is also developed, and a concise 3dB input intercept point (IIP3) formula incorporating the drain induced non-linearity in terms of the voltage gain is presented. The proposed models are validated with DC and AC simulations and measurements.

Power consumption is of utmost importance in portable devices as they are operated from a limited energy supply. Wireless radio is one of the most power-hungry blocks in these systems and needs to be activated opportunistically. Radio frequency (RF) spectrum sensing can be performed with a dedicated low power radio to control the main wireless radio. A low power receiver architecture is proposed in this work to monitor the 2.4GHz Industrial, Scientific, and Medical (ISM) band for communication standards such as Wireless Local Area Network (WLAN), Zonal Intercommunication Global-standard (ZigBee), and Bluetooth-Low-Energy (BLE). The whole ISM band is down-converted to zero-intermediate-frequency (ZIF) and a tunable base-band complex band-pass filter (CBPF) is used to scan the spectrum. A widely tunable first order ultra-low-power transconductor-capacitor (Gm-C) CBPF architecture was designed and fabricated in GF 22FDX technology, which occupies 0.0049mm 2 area. A frequency shift of ±60MHz was achieved with 5-40MHz bandwidth range. Power consumption ranges from 6.7$\mu \text{W}$to 99.2$\mu \text{W}$with the best and the worst figure-of-merit of 0.034fJ/pole and 0.082fJ/pole that is the energy consumption per pole normalized by the spurious free dynamic range.

Audio processing had been in demand throughout the electronic era. Recent advances in neural networks increased the demand on audio processing for speech recognition applications. In this work, a rigorous study on the dynamic range and system complexity optimization is presented for a mixed-signal keyword spotting system. The proposed system consists of an analog feature extractor and a neural network based keyword classifier. The results showed that with the proposed method, more than an order of magnitude power saving can be achieved in the analog feature extraction compared to the digital state-of-the-art counterpart.