Effective number of bits

About: Effective number of bits is a research topic. Over the lifetime, 3776 publications have been published within this topic receiving 46130 citations.

Method and apparatus for continuously controlling the dynamic range from an analog-to-digital converter

Abstract: A method for continuously determining the required dynamic range for an analog-to-digital converter by determining the received signal strength and using this received signal strength value in combination with the overall dynamic range for the ADC and the target resolution of the ADC to decode a radio channel in the absence of interference, wherein the target resolution is also related to the type of decoding to be performed subsequent to analog-to-digital conversion. The method allows for a reduction in power consumption associated with the ADC, especially when the incoming signal is received with few interfering radio channels and with a relatively high signal strength. The present method can be combined with gain control and analog alert detection.

A 1.35 GS/s, 10 b, 175 mW Time-Interleaved AD Converter in 0.13 µm CMOS

Abstract: A time-interleaved ADC is presented with 16 channels, each consisting of a track-and-hold (T&H) and two successive approximation (SA) ADCs in a pipeline configuration to combine a high sample rate with good power efficiency. The single-sided overrange architecture achieves a 25% higher power efficiency of the SA-ADC compared with the conventional overrange architecture, and look-ahead logic is used to minimize logic delay in the SA-ADC. For the T&H, three techniques are presented enabling a high bandwidth and linearity and good timing alignment. Single channel performance of the ADC is 6.9 ENOB at an input frequency of 4 GHz. Multichannel performance is 7.7 ENOB at 1.35 GS/s with an ERBW of 1 GHz. The FoM of the complete ADC including T&H is 0.6 pJ per conversion step. An improved version is presented as well and achieves an SNDR of 8.6 ENOB for low sample rates, and, with increased supply voltage, it reaches a sample rate of 1.8 GS/s with 7.9 ENOB at low input frequencies and an ERBW of 1 GHz. At fin = 3.6 GHz, the SNDR is still 6.5 ENOB, and total timing error including jitter is 0.4 ps rms.

A Low-Power 32-Channel Digitally Programmable Neural Recording Integrated Circuit

Abstract: We report the design of an ultra-low-power 32-channel neural-recording integrated circuit (chip) in a 0.18 μ m CMOS technology. The chip consists of eight neural recording modules where each module contains four neural amplifiers, an analog multiplexer, an A/D converter, and a serial programming interface. Each amplifier can be programmed to record either spikes or LFPs with a programmable gain from 49-66 dB. To minimize the total power consumption, an adaptive-biasing scheme is utilized to adjust each amplifier's input-referred noise to suit the background noise at the recording site. The amplifier's input-referred noise can be adjusted from 11.2 μVrms (total power of 5.4 μW) down to 5.4 μVrms (total power of 20 μW) in the spike-recording setting. The ADC in each recording module digitizes the a.c. signal input to each amplifier at 8-bit precision with a sampling rate of 31.25 kS/s per channel, with an average power consumption of 483 nW per channel, and, because of a.c. coupling, allows d.c. operation over a wide dynamic range. It achieves an ENOB of 7.65, resulting in a net efficiency of 77 fJ/State, making it one of the most energy-efficient designs for neural recording applications. The presented chip was successfully tested in an in vivo wireless recording experiment from a behaving primate with an average power dissipation per channel of 10.1 μ W. The neural amplifier and the ADC occupy areas of 0.03 mm2 and 0.02 mm2 respectively, making our design simultaneously area efficient and power efficient, thus enabling scaling to high channel-count systems.

A 1.8-V 1.6-GSample/s 8-b self-calibrating folding ADC with 7.26 ENOB at Nyquist frequency

Abstract: This study demonstrates for the first time the significant performance enhancement that calibration brings to folding-interpolating analog-to-digital converters (ADCs). The resulting 1.8-V ADC in 0.18-/spl mu/m CMOS achieves a conversion rate exceeding 1.6 GSample/s, since the amplifier device sizes can be minimized to maximize speed without the restriction of device matching. At 1.6 GS/s, the ADC achieves 0.15 LSB DNL, 0.35 LSB INL, 7.6 effective number of bits (ENOBs) at 100 MHz input, and 7.26 ENOB at Nyquist. At this speed, current consumption from a single 1.8-V supply is 245 mA analog, 185 mA digital, and 90 mA for the LVDS drivers. The ac performance is approximately 1.5 ENOBs higher compared to the same circuit with calibration disabled. The use of best design practices to optimize the ADC linearity prior to introducing calibration resulted in this small required dynamic calibration range, simplifying the calibrator circuitry and resulting in stable continuous performance over time without recalibration. Therefore, the fully on-chip calibration is performed automatically, just one time at power-up.

Regime Change: Bit-Depth Versus Measurement-Rate in Compressive Sensing

Abstract: The recently introduced compressive sensing (CS) framework enables digital signal acquisition systems to take advantage of signal structures beyond bandlimitedness. Indeed, the number of CS measurements required for stable reconstruction is closer to the order of the signal complexity than the Nyquist rate. To date, the CS theory has focused on real-valued measurements, but in practice measurements are mapped to bits from a finite alphabet. Moreover, in many potential applications the total number of measurement bits is constrained, which suggests a tradeoff between the number of measurements and the number of bits per measurement. We study this situation in this paper and show that there exist two distinct regimes of operation that correspond to high/low signal-to-noise ratio (SNR). In the measurement compression (MC) regime, a high SNR favors acquiring fewer measurements with more bits per measurement; in the quantization compression (QC) regime, a low SNR favors acquiring more measurements with fewer bits per measurement. A surprise from our analysis and experiments is that in many practical applications it is better to operate in the QC regime, even acquiring as few as 1 bit per measurement.