Combining M analog-to-digital converters (ADC) in parallel increases the maximum signal-to-noise ratio (SNR) by a factor of M, assuming the noise is uncorrelated from one channel to the next. This allows for a significant increase in SNR over a single ADC; however, noise and harmonic correlation degrade this improvement. ADCs have three sources of noise: thermal (and other random physical processes), sampling, and quantization noise. There are two system components creating harmonics: the sampler and the quantizer. In this thesis, I determine, analytically and experimentally, the degree of correlation between signals, noise, and harmonics in a parallel ADC array.

To test the analysis experimentally, I developed a 16-channel test-bed using 16-bit, state-of-the-art ADCs and 16 direct-digital synthesizers as low-noise signal sources. The test bed provides excellent signal isolation between channels and minimal digital noise to enable the measurement of very low levels of correlation. I investigated the feasibility of measuring the very high levels of signal correlation in the presence of channel nonlinearities with different measurement signals. For a completely linear channel, the channel matching is limited by noise. With nonlinearities, the ability to measure the signal correlation depends on the measurement signal. I verified that the thermal noise is uncorrelated across 16 channels as expected. I also demonstrated that sampling noise is fully correlated from channel-to-channel when a common clock drives the ADCs. Efforts to reduce the correlation using two previously developed de-correlation techniques-phase randomization and frequency offsets-successfully reduced the correlated noise by a factor of two. I then demonstrated analytically and experimentally that harmonics from quantizers are largely uncorrelated; however, harmonics from the sampler are largely correlated confirming the need for decorrelation techniques. I demonstrated the impact of the previously developed decorrelation techniques to reduce harmonic correlation and developed two new decorrelation techniques: phase cancellation and clock offsets, which offer significant advantages over phase randomization and frequency offsets. Each technique offers different levels of dynamic range improvement and complexity, allowing for a range of techniques to target the optimal level of decorrelation.