A Millimeter-Wave Coexistent RFIC Receiver Architecture in 0.18-µm SiGe BiCMOS for Radar and Communication Systems

Innovative circuit architectures and techniques to enhance the performance of several key BiCMOS RFIC building blocks applied in radar and wireless communication systems operating at the millimeter-wave frequencies are addressed in this dissertation. The former encapsulates the development of an advanced, low-cost and miniature millimeter-wave coexistent current mode direct conversion receiver for short-range, high-resolution radar and high data rate communication systems. A new class of broadband low power consumption active balun-LNA consisting of two common emitters amplifiers mutually coupled thru an AC stacked transformer for power saving and gain boosting. The active balun-LNA exhibits new high linearity technique using a constant gm cell transconductance independent of input-outputs variations based on equal emitters’ area ratios. A novel multi-stages active balun-LNA with innovative technique to mitigate amplitude and phase imbalances is proposed. The new multi-stages balun-LNA technique consists of distributed feed-forward averaging recycles correction for amplitude and phase errors and is insensitive to unequal paths parasitic from input to outputs. The distributed averaging recycles correction technique resolves the amplitude and phase errors residuals in a multi-iterative process. The new multi-stages balun-LNA averaging correction technique is frequency independent and can perform amplitude and phase calibrations without relying on passive lumped elements for compensation. The multi-stage balun-LNA exhibits excellent performance from 10 to 50 GHz with amplitude and phase mismatches less than 0.7 dB and 2.86º, respectively. Furthermore, the new multi-stages balun-LNA operates in current mode and shows high linearity with low power consumption. The unique balun-LNA design can operates well into mm-wave regions and is an integral block of the mm-wave radar and communication systems. The integration of several RFIC blocks constitutes the broadband millimeter-wave coexistent current mode direct conversion receiver architecture operating from 22- 44 GHz. The system and architectural level analysis provide a unique understanding into the receiver characteristics and design trade-offs. The RF front-end is based on the broadband multi-stages active balun-LNA coupled into a fully balanced passive mixer with an all-pass in-phase/quadrature phase generator. The trans-impedance amplifier converts the input signal current into a voltage gain at the outputs. Simultaneously, the high power input signal current is channelized into an anti-aliasing filter with 20 dB rejection for out of band interferers. In addition, the dissertation demonstrates a wide dynamic range system with small die area, cost effective and very low power consumption

A Millimeter-Wave Coexistent RFIC Receiver Architecture in 0.18-µm SiGe BiCMOS for Radar and Communication Systems

Innovative circuit architectures and techniques to enhance the performance of several key BiCMOS RFIC building blocks applied in radar and wireless communication systems operating at the millimeter-wave frequencies are addressed in this dissertation. The former encapsulates the development of an advanced, low-cost and miniature millimeter-wave coexistent current mode direct conversion receiver for short-range, high-resolution radar and high data rate communication systems. A new class of broadband low power consumption active balun-LNA consisting of two common emitters amplifiers mutually coupled thru an AC stacked transformer for power saving and gain boosting. The active balun-LNA exhibits new high linearity technique using a constant gm cell transconductance independent of input-outputs variations based on equal emitters’ area ratios. A novel multi-stages active balun-LNA with innovative technique to mitigate amplitude and phase imbalances is proposed. The new multi-stages balun-LNA technique consists of distributed feed-forward averaging recycles correction for amplitude and phase errors and is insensitive to unequal paths parasitic from input to outputs. The distributed averaging recycles correction technique resolves the amplitude and phase errors residuals in a multi-iterative process. The new multi-stages balun-LNA averaging correction technique is frequency independent and can perform amplitude and phase calibrations without relying on passive lumped elements for compensation. The multi-stage balun-LNA exhibits excellent performance from 10 to 50 GHz with amplitude and phase mismatches less than 0.7 dB and 2.86º, respectively. Furthermore, the new multi-stages balun-LNA operates in current mode and shows high linearity with low power consumption. The unique balun-LNA design can operates well into mm-wave regions and is an integral block of the mm-wave radar and communication systems. The integration of several RFIC blocks constitutes the broadband millimeter-wave coexistent current mode direct conversion receiver architecture operating from 22- 44 GHz. The system and architectural level analysis provide a unique understanding into the receiver characteristics and design trade-offs. The RF front-end is based on the broadband multi-stages active balun-LNA coupled into a fully balanced passive mixer with an all-pass in-phase/quadrature phase generator. The trans-impedance amplifier converts the input signal current into a voltage gain at the outputs. Simultaneously, the high power input signal current is channelized into an anti-aliasing filter with 20 dB rejection for out of band interferers. In addition, the dissertation demonstrates a wide dynamic range system with small die area, cost effective and very low power consumption

Architectures and Design of VLSI Machine Learning Systems

Quintillions of bytes of data are generated every day in this era of big data. Machine learning techniques are utilized to perform predictive analysis on these data, to reveal hidden relationships and dependencies and perform predictions of outcomes and behaviors. The obtained predictive models are used to interpret the existing data and predict new data information. Nowadays, most machine learning algorithms are realized by software programs running on general-purpose processors, which usually takes a huge amount of CPU time and introduces unbelievably high energy consumption. In comparison, a dedicated hardware design is usually much more efficient than software programs running on general-purpose processors in terms of runtime and energy consumption. Therefore, the objective of this dissertation is to develop efficient hardware architectures for mainstream machine learning algorithms, to provide a promising solution to addressing the runtime and energy bottlenecks of machine learning applications. However, it is a really challenging task to map complex machine learning algorithms to efficient hardware architectures. In fact, many important design decisions need to be made during the hardware development for efficient tradeoffs. In this dissertation, a parallel digital VLSI architecture for combined SVM training and classification is proposed. For the first time, cascade SVM, a powerful training algorithm, is leveraged to significantly improve the scalability of hardware-based SVM training and develop an efficient parallel VLSI architecture. The parallel SVM processors provide a significant training time speedup and energy reduction compared with the software SVM algorithm running on a general-purpose CPU. Furthermore, a liquid state machine based neuromorphic learning processor with integrated training and recognition is proposed. A novel theoretical measure of computational power is proposed to facilitate fast design space exploration of the recurrent reservoir. Three low-power techniques are proposed to improve the energy efficiency. Meanwhile, a 2-layer spiking neural network with global inhibition is realized on Silicon. In addition, we also present architectural design exploration of a brain-inspired digital neuromorphic processor architecture with memristive synaptic crossbar array, and highlight several synaptic memory access styles. Various analog-to-digital converter schemes have been investigated to provide new insights into the tradeoff between the hardware cost and energy consumption