Review of ADCs for imaging

The aim of this article is to guide image sensors designers to optimize the analog-to-digital conversion of pixel outputs. The most common ADCs topologies for image sensors are presented and discussed. The ADCs specific requirements for these sensors are analyzed and quantified. Finally, we present relevant recent contributions of specific ADCs for image sensors and we compare them using a novel FOM. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use onlyPeer reviewe

Review of ADCs for imaging

The aim of this article is to guide image sensors designers to optimize the analog-to-digital conversion of pixel outputs. The most common ADCs topologies for image sensors are presented and discussed. The ADCs specific requirements for these sensors are analyzed and quantified. Finally, we present relevant recent contributions of specific ADCs for image sensors and we compare them using a novel FOM

Micropower incremental analog-to-digital converters

Incremental ADCs (IADCs) have many advantages for low-frequency high-accuracy data conversion—they are easy to multiplex between channels, need simpler digital decimation filter, and allow extended counting with a Nyquist-rate ADC. A single-loop incremental ADC was designed and fabricated in 90 nm for a biosensor interface circuit. It incorporates one integrator, and uses noise-coupling technique to achieve second-order noise-shaping. The use of feed-forward coupling and multi-bit internal quantizer allows low swing at the integrator, and hence low-power operation. The measured SNR is 74 dB within a signal bandwidth 2 kHz, and a 14 μW power consumption. A new two-step IADC was proposed for 250 Hz bandwidth sensor interface circuits. It extends the order of a conventional incremental ADC from N to (2N-1) by way of a two-step operation. However, it only needs the same circuitry as the Nth-order IADC. A second-order loop filter was designed and fabricated by 2.5V I/O devices in 65 nm to demonstrate this concept to achieve third-order noise-shaping performance. Operated at sampling frequency 96 kHz, the measured dynamic range is 99.8 dB relative to a maximum input 2.2 VPP. The measured maximum SNDR was 91 dB with a 2.2 V[subscript PP] input amplitude. The ADC core area is 0.2 mm², and the IADC consumed only 11.7 μW. A new incremental ADC with multi-step extended-counting was proposed for sensor interface conversion. A 1st-order feedforward modulator was used for the coarse conversion, and the residue voltage was quantized by re-using the modulator for the fine conversion. Then, the circuit was re-configured as a counting ADC to quantize the residue voltage. The three steps of the circuits perform 15-bit quantization by 5-bit/step. A first-order IADC could only achieve 6.6-bit performance within the same conversion time of 96 clock periods. Reusing the first-order circuits, extra 8.4-bit is thus achieved

Power Efficient Architectures for High Accuracy Analog-to-Digital Converters

Incremental ADCs (IADCs) have found wide applications in sensor interface circuitry since, compared to ∆Σ ADCs, they provide low-latency high-accuracy conversion and easy multiplexing among multiple channels. On the other hand, continuous-time ∆Σ ADCs (CTDSM) have been receiving more and more attention as a power-efficient solution in targeting medium to high accuracy over wider range of signal bandwidth (tens of MHz). In this dissertation, novel configurations have been explored in both architectures for power-efficient and high-accuracy data conversion. First, a multi-step incremental ADC (IADC) using multi-slope extended counting technique is described. Only one active integrator is used in the three-step conversion cycle. The accuracy of the IADC is extended by having it configured as multi-slope ADCs in two additional steps. The proposed IADC uses the same circuitry as a first-order IADC (IADC1), but it exhibits as good efficiency as its second-order ∆Σ ADC counterpart. For the same accuracy, the conversion cycle is shortened by a factor of more than 2⁹ compared to the IADC1. Fabricated in 0.18-μm CMOS process, the prototype ADC occupies 0.5 mm². With a 642 kHz clock, it achieves SNDR of 52.2 dB in the first step. The SNDR is boosted to 79.8 dB in the second step, and to 96.8 dB in the third step, over a 1 kHz signal band. The power consumption is 35 µW from a 1.5 V power supply. This gives an excellent Schreier FoM of 174.6 dB. Secondly, a multi-step incremental ADC with extended binary counting is proposed. It achieves high accuracy by splitting one conversion cycle into two serial steps. During the first step, the ADC works as a first-order incremental ADC (IADC1). The second step reuses the single integrator and extends the accuracy to 16 bits by a two-capacitor SAR-assisted binary counting technique. For the same accuracy, the conversion cycle is shortened by a factor of more than 2⁸ as compared to the single-step IADC. Fabricated in 0.18-μm CMOS process, the SAR-assisted IADC achieves a peak SNR/SNDR/DR of 97.1/96.6/100.2 dB over a 1.2 kHz bandwidth, while dissipating 33.2 μW from a 1.5 V supply. This gives a Schreier FoM of 175.8 dB and Walden FoM of 0.25 pJ/conv.-step. Finally, the design of a continuous-time ∆Σ modulator (CTDSM) to be used in an ultrasound beamformer for biomedical imaging is described. To achieve better resolution, the prototype modulator operates at 1.2 GHz. It incorporates a digital excess loop delay (ELD) compensation to replace the active adder in front of the internal quantizer. A digitally controlled reference-switching matrix, combined with the data-weighted averaging (DWA) technique, results in a delay-free feedback path. A multi-bit FIR feedback DAC, along with its compensation path, is used to achieve lower clock jitter sensitivity and better loop filter linearity. The modulator achieves 79.4 dB dynamic range, 77.3 dB SNR and 74.3 dB SNDR over a 15 MHz signal bandwidth. Fabricated in a 65 nm CMOS process, the core modulator occupies an area of only 0.16 mm² and dissipates 6.96 mW from a 1 V supply. A 58.6 fJ/conversion-step figure of merit was achieved.Keywords: Incremental ADC, multi-step operation, instrumentation and measurement, sensor interface, analog-to-digital converter, extended counting, chopper stabilization, delta-sigma ADC, multi-slope ADCsKeywords: Incremental ADC, multi-step operation, instrumentation and measurement, sensor interface, analog-to-digital converter, extended counting, chopper stabilization, delta-sigma ADC, multi-slope ADC

Smart Sensor Networks For Sensor-Neural Interface

One in every fifty Americans suffers from paralysis, and approximately 23% of paralysis cases are caused by spinal cord injury. To help the spinal cord injured gain functionality of their paralyzed or lost body parts, a sensor-neural-actuator system is commonly used. The system includes: 1) sensor nodes, 2) a central control unit, 3) the neural-computer interface and 4) actuators. This thesis focuses on a sensor-neural interface and presents the research related to circuits for the sensor-neural interface. In Chapter 2, three sensor designs are discussed, including a compressive sampling image sensor, an optical force sensor and a passive scattering force sensor. Chapter 3 discusses the design of the analog front-end circuit for the wireless sensor network system. A low-noise low-power analog front-end circuit in 0.5μm CMOS technology, a 12-bit 1MS/s successive approximation register (SAR) analog-to-digital converter (ADC) in 0.18μm CMOS process and a 6-bit asynchronous level-crossing ADC realized in 0.18μm CMOS process are presented. Chapter 4 shows the design of a low-power impulse-radio ultra-wide-band (IR-UWB) transceiver (TRx) that operates at a data rate of up to 10Mbps, with a power consumption of 4.9pJ/bit transmitted for the transmitter and 1.12nJ/bit received for the receiver. In Chapter 5, a wireless fully event-driven electrogoniometer is presented. The electrogoniometer is implemented using a pair of ultra-wide band (UWB) wireless smart sensor nodes interfacing with low power 3-axis accelerometers. The two smart sensor nodes are configured into a master node and a slave node, respectively. An experimental scenario data analysis shows higher than 90% reduction of the total data throughput using the proposed fully event-driven electrogoniometer to measure joint angle movements when compared with a synchronous Nyquist-rate sampling system. The main contribution of this thesis includes: 1) the sensor designs that emphasize power efficiency and data throughput efficiency; 2) the fully event-driven wireless sensor network system design that minimizes data throughput and optimizes power consumption