Methodology for Mismatch Reduction in Time-Interleaved ADCs

This paper presents a methodology to minimize mismatch errors in time-interleaved analog-to-digital converters (ADC) by means of averaging multiple channels. A simple algorithm improving both spurious free dynamic range (SFDR) and signal-to-noise and distortion ratio (SINAD) is demonstrated. The presented technique provides robustness against inaccurately identified mismatch errors and does not require computationally expensive post-processing of the signal

Architectural Improvements Towards an Efficient 16-18 Bit 100-200 MSPS ADC

As Data conversion systems continue to improve in speed and resolution, increasing demands are placed on the performance of high-speed Analog to Digital Conversion systems. This work makes a survey about all these and proposes a suitable architecture in order to achieve the desired specifications of 100-200MS/s with 16-18 bit of resolution. The main architecture is based on paralleled structures in order to achieve high sampling rate and at the same time high resolution. In order to solve problems related to Time-interleaved architectures, an advanced randomization method was introduced. It combines randomization and spectral shaping of mismatches. With a simple low-pass filter the method can, compared to conventional randomization algorithms, improve the SFDR as well as the SINAD. The main advantage of this technique over previous ones is that, because the algorithm only need that ADCs are ordered basing on their time mismatches, the absolute accuracy of the mismatch identification method does not matter and, therefore, the requirements on the timing mismatch identification are very low. In addition to that, this correction system uses very simple algorithms able to correct not only for time but also for gain and offset mismatches

Offset mismatch calibration for TI-ADCs in high-speed OFDM systems

Time-interleaved analog-to-digital converters (TIADCs) are widely used for multi-Gigabit orthogonal frequency division multiplexing (OFDM) based systems because of their attractive high sampling rate and high resolution. However, when not perfectly calibrated, mismatches such as offset mismatch, gain mismatch and timing mismatch between parallel sub-ADCs can significantly degrade the system performance. In this paper, we focus on offset mismatch. We analyze two calibration techniques for the offset mismatch, based on the least-squares (LS) and linear minimum mean-squared error (LMMSE) algorithms assuming an AWGN channel. The simulation results show that our method is capable of improving the BER performance. As expected, the LMMSE estimator outperforms the LS estimator. However, at large offset mismatch levels or low noise level, both estimators converge. In this paper, we derive the condition on the mismatch level for convergence between the two estimators

A 16-b 10Msample/s Split-Interleaved Analog to Digital Converter

This work describes the integrated circuit design of a 16-bit, 10Msample/sec, combination ‘split’ interleaved analog to digital converter. Time interleaving of analog to digital converters has been used successfully for many years as a technique to achieve faster speeds using multiple identical converters. However, efforts to achieve higher resolutions with this technique have been difficult due to the precise matching required of the converter channels. The most troublesome errors in these types of converters are gain, offset and timing differences between channels. The ‘split ADC’ is a new concept that allows the use of a deterministic, digital, self calibrating algorithm. In this approach, an ADC is split into two paths, producing two output codes from the same input sample. The difference of these two codes is used as the calibration signal for an LMS error estimation algorithm that drives the difference error to zero. The ADC is calibrated when the codes are equal and the output is taken as the average of the two codes. The ‘split’ ADC concept and interleaved architecture are combined in this IC design to form the core of a high speed, high resolution, and self-calibrating ADC system. The dual outputs are used to drive a digital calibration engine to correct for the channel mismatch errors. This system has the speed benefits of interleaving while maintaining high resolution. The hardware for the algorithm as well as the ADC can be implemented in a standard 0.25um CMOS process, resulting in a relatively inexpensive solution. This work is supported by grants from Analog Devices Incorporated (ADI) and the National Science Foundation (NSF)

Analog‐to‐Digital Conversion for Cognitive Radio: Subsampling, Interleaving, and Compressive Sensing

This chapter explores different analog-to-digital conversion techniques that are suitable to be implemented in cognitive radio receivers. This chapter details the fundamentals, advantages, and drawbacks of three promising techniques: subsampling, interleaving, and compressive sensing. Due to their major maturity, subsampling- and interleaving-based systems are described in further detail, whereas compressive sensing-based systems are described as a complement of the previous techniques for underutilized spectrum applications. The feasibility of these techniques as part of software-defined radio, multistandard, and spectrum sensing receivers is demonstrated by proposing different architectures with reduced complexity at circuit level, depending on the application requirements. Additionally, the chapter proposes different solutions to integrate the advantages of these techniques in a unique analog-to-digital conversion process

Time-Interleaved Analog-to-Digital Converter (TIADC) Compensation Using Multichannel Filters

Published methods that employ a filter bank for compensating the timing and bandwidth mismatches of an M-channel time-interleaved analog-to-digital converter (TIADC) were developed based on the fact that each sub-ADC channel is a downsampled version of the analog input. The output of each sub-ADC is filtered in such a way that, when all the filter outputs are summed, the aliasing components are minimized. If each channel of the filter bank has N coefficients, the optimization of the coefficients requires computing the inverse of an MN times MN matrix if the weighted least squares (WLS) technique is used as the optimization tool. In this paper, we present a multichannel filtering approach for TIADC mismatch compensation. We apply the generalized sampling theorem to directly estimate the ideal output of each sub-ADC using the outputs of all the sub-ADCs. If the WLS technique is used as the optimization tool, the dimension of the matrix to be inversed is N times N. For the same number of coefficients (and also the same spurious component performance given sufficient arithmetic precision), our technique is computationally less complex and more robust than the filter-bank approach. If mixed integer linear programming is used as the optimization tool to produce filters with coefficient values that are integer powers of two, our technique produces a saving in computing resources by a factor of approximately (100.2N(M- 1)/(M-1) in the TIADC filter design.published_or_final_versio