Time-encoding analog-to-digital converters : bridging the analog gap to advanced digital CMOS? Part 2: architectures and circuits

The scaling of CMOS technology deep into the nanometer range has created challenges for the design of highperformance analog ICs: they remain large in area and power consumption in spite of process scaling. Analog circuits based on time encoding [1], [2], where the signal information is encoded in the waveform transitions instead of its amplitude, have been developed to overcome these issues. While part one of this overview article [3] presented the basic principles of time encoding, this follow-up article describes and compares the main time-encoding architectures for analog-to-digital converters (ADCs) and discusses the corresponding design challenges of the circuit blocks. The focus is on structures that avoid, as much as possible, the use of traditional analog blocks like operational amplifiers (opamps) or comparators but instead use digital circuitry, ring oscillators, flip-flops, counters, an so on. Our overview of the state of the art will show that these circuits can achieve excellent performance. The obvious benefit of this highly digital approach to realizing analog functionality is that the resulting circuits are small in area and more compatible with CMOS process scaling. The approach also allows for the easy integration of these analog functions in systems on chip operating at "digital" supply voltages as low as 1V and lower. A large part of the design process can also be embedded in a standard digital synthesis flow

Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs

In recent years, thanks to the development of integrated circuits, clinical medicine has witnessed significant advancements, enabling more efficient and intelligent treatment approaches. Particularly in the field of neuromedical, the utilization of brain-machine interfaces (BMI) has revolutionized the treatment of neurological diseases such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. The BMI acquires neural signals via recording circuits and analyze them to regulate neural stimulator circuits for effective neurological treatment. However, traditional BMI designs, which are often isolated, have given way to closed-loop brain-machine interfaces (CL-BMI) as a contemporary development trend. CL-BMI offers increased integration and accelerated response speed, marking a significant leap forward in neuromedicine. Nonetheless, this advancement comes with its challenges, notably the stimulation artifacts (SA) problem inherent to the structural characteristics of CL-BMI, which poses significant challenges on the neural recording front-ends (NRFE) site. This paper aims to provide a comprehensive overview of technologies addressing artifacts in the NRFE site within CL-BMI. Topics covered will include: (1) understanding and assessing artifacts; (2) exploring the impact of artifacts on traditional neural recording front-ends; (3) reviewing recent technological advancements aimed at addressing artifact-related issues; (4) summarizing and classifying the aforementioned technologies, along with an analysis of future trends

RF MEMS reference oscillators platform for wireless communications

A complete platform for RF MEMS reference oscillator is built to replace bulky quartz from mobile devices, thus reducing size and cost. The design targets LTE transceivers. A low phase noise 76.8 MHz reference oscillator is designed using material temperature compensated AlN-on-silicon resonator. The thesis proposes a system combining piezoelectric resonator with low loading CMOS cross coupled series resonance oscillator to reach state-of-the-art LTE phase noise specifications. The designed resonator is a two port fundamental width extensional mode resonator. The resonator characterized by high unloaded quality factor in vacuum is designed with low temperature coefficient of frequency (TCF) using as compensation material which enhances the TCF from - 3000 ppm to 105 ppm across temperature ranges of -40˚C to 85˚C. By using a series resonant CMOS oscillator, phase noise of -123 dBc/Hz at 1 kHz, and -162 dBc/Hz at 1MHz offset is achieved. The oscillator’s integrated RMS jitter is 106 fs (10 kHz–20 MHz), consuming 850 μA, with startup time is 250μs, achieving a Figure-of-merit (FOM) of 216 dB. Electronic frequency compensation is presented to further enhance the frequency stability of the oscillator. Initial frequency offset of 8000 ppm and temperature drift errors are combined and further addressed electronically. A simple digital compensation circuitry generates a compensation word as an input to 21 bit MASH 1 -1-1 sigma delta modulator incorporated in RF LTE fractional N-PLL for frequency compensation. Temperature is sensed using low power BJT band-gap front end circuitry with 12 bit temperature to digital converter characterized by a resolution of 0.075˚C. The smart temperature sensor consumes only 4.6 μA. 700 MHz band LTE signal proved to have the stringent phase noise and frequency resolution specifications among all LTE bands. For this band, the achieved jitter value is 1.29 ps and the output frequency stability is 0.5 ppm over temperature ranges from -40˚C to 85˚C. The system is built on 32nm CMOS technology using 1.8V IO device

Energy-efficient data converter design in scaled CMOS technology

Data converters bridge the physical and digital worlds. They have been the crucial building blocks in modern electronic systems, and are expected to have a growing significance in the booming era of Internet-of-Things (IoT) and 5G communications. The applications raise energy-efficiency requirements for both low-speed and high-speed converters since they are widely deployed in wireless sensor nodes and portable devices. To explore the solutions, the author worked on three directions: 1) techniques to improve the efficiency of the low-speed converters including the comparator; 2) techniques to develop high-speed data converters including the reference stabilization; 3) new architecture to improve the efficiency of the capacitance-to-digital converter (CDC). In the first part, a power-efficient 10-bit SAR ADC featured with a gain-boosted dynamic comparator is presented. In energy-constrained applications, the converter is usually supplied with low supply voltage (e.g., 0.3 V-0.5 V), which reduces the comparator pre-amplifier (pre-amp) gain and results in higher noise. A novel comparator topology with a dynamic common-gate stage is proposed to increase the pre-amplification gain, thereby reducing noise and offset. Besides, statistical estimation and loading switching techniques are combined to further improve energy efficiency. A 40-nm CMOS prototype achieves a Walden FoM of 1.5 fJ/conversion-step while operating at 100-kS/s from a 0.5-V supply. To further improve the energy-efficiency of the comparator, a novel dynamic pre-amp is proposed. By using an inverter-based input pair powered by a floating reservoir capacitor, the pre-amp realizes both current reuse and dynamic bias, thereby significantly boosting g [subscript m] /I [subscript D] and reducing noise. Moreover, it greatly reduces the influence of the input common-mode (CM) voltage on the comparator performance, including noise, offset, and delay. A prototype comparator in 180-nm achieves 46-μV input-referred noise while consuming only 1 pJ per comparison under 1.2-V supply, which represents greater than 7 times energy efficiency boost compared to that of a Strong-Arm (SA) latch. The second part of this dissertation focuses on high-speed data converter techniques. A 10-bit high-speed two-stage loop-unrolled SAR ADC is presented. To reduce the SAR logic delay and power, each bit uses a dedicated comparator to store its output and generate an asynchronous clock for the next comparison. To suppress the comparator offset mismatch induced non-linearity, a shared pre-amp are employed in the second fine stage, which is implemented by a dynamic latch to avoid static power consumption. The prototype ADC in 40-nm CMOS achieves 55-dB peak SNDR at 200-MS/s sampling rate without any calibration. A key limiting factor for the SAR ADC to simultaneously achieve high speed and high resolution is the reference ripple settling problem caused by DAC switching. Unlike prior techniques that aim to minimize the reference ripple which requires large reference buffer power or on-chip decoupling capacitance area, this work proposes a new perspective: it provides an extra path for the full-sized reference ripple to couple to the comparator but with an opposite polarity, so that the effect of the reference ripple is canceled out, thus ensuring an accurate conversion result. The prototype 10-bit 120-MS/s SAR ADC is fabricated in 40-nm CMOS process and achieves an SNDR of 55 dB with only 3 pF reference decoupling capacitor. Finally, this dissertation also presents the design of an incremental time-domain two-step CDC. Unlike the classic two-step CDC, this work replaces the OTA-based active-RC integrator with a VCO-based integrator and performs time domain (TD) ΔΣ modulation. The VCO is mostly digital and consumes low power. Featuring the infinite DC gain in phase domain and intrinsic spatial phase quantization, this TDΔΣ enables a CDC design, achieving 85-dB SQNR by having only a 4-bit quantizer, a 1st-order loop and a low OSR of 15. The prototype fabricated in 40-nm CMOS achieves a resolution of 0.29 fF while dissipating only 0.083 nJ per conversion, which improves the energy efficiency by greater than 2 times comparing to that of state-of-the-art CDCsElectrical and Computer Engineerin

A 23μW Solar-Powered Keyword-Spotting ASIC with Ring-Oscillator-Based Time-Domain Feature Extraction

Voice-controlled interfaces on acoustic Internet-of-Things (IoT) sensor nodes and mobile devices require integrated low-power always-on wake-up functions such as Voice Activity Detection (VAD) and Keyword Spotting (KWS) to ensure longer battery life. Most VAD and KWS ICs focused on reducing the power of the feature extractor (FEx) as it is the most power-hungry building block. A serial Fast Fourier Transform (FFT)-based KWS chip [1] achieved 510nW; however, it suffered from a high 64ms latency and was limited to detection of only 1-to-4 keywords (2-to-5 classes). Although the analog FEx [2]–[3] for VAD/KWS reported 0.2μW-to-1 μW and 10ms-to-100ms latency, neither demonstrated >5 classes in keyword detection. In addition, their voltage-domain implementations cannot benefit from process scaling because the low supply voltage reduces signal swing; and the degradation of intrinsic gain forces transistors to have larger lengths and poor linearity