Two improved methods for testing ADC parametric faults by digital input signals

In this paper, two improved methods are presented extending our previous work. The first one improves the results by adjusting the voltage levels of the input pulse wave stimulus. Compared with the sine wave input stimulus, the four-level pulse wave can detect even more faulty cases with the offset faults. The second one improves the results by calculating the similarity of the output spectra between the golden devices and the DUTs. Compared with the previous method [10], it is less sensitive to the jitter and the change of the rise/fall time of the input pulse wave stimulus. In these two methods, a number of golden devices are tested at first to obtain the fault-free range. At last, a signature result is obtained from both methods. It can filter out the faulty devices in a quick way before testing the specific values of the conventional dynamic and static parameters

Improved method for SNR prediction in machine-learning-based test

This paper applies an improved method for testing the signal-to-noise ratio (SNR) of Analogue-to-Digital Converters (ADC). In previous work, a noisy and nonlinear pulse signal is exploited as the input stimulus to obtain the signature results of ADC. By applying a machine-learning-based approach, the dynamic parameters can be predicted by using the signature results. However, it can only estimate the SNR accurately within a certain range. In order to overcome this limitation, an improved method based on work is applied in this work. It is validated on the Labview model of a 12-bit 80 Ms/s pipelined ADC with a pulse- wave input signal of 3 LSB noise and 7-bit nonlinear rising and falling edges

The test ability of an adaptive pulse wave for ADC testing

In the conventional ADC production test method, a high-quality analogue sine wave is applied to the Analogue-to-Digital Converter (ADC), which is expensive to generate. Nowadays, an increasing number of ADCs are integrated into a system-on-chip (SoC) platform design, which usually contains a digital embedded processor. In such a platform, a digital pulse wave is obviously less expensive to generate than an accurate analogue sine wave. As a result, the usage of a digital pulse wave has been investigated to test ADCs as the test stimulus. In this paper, the ability of a digital adaptive pulse wave for ADC testing is presented via the measurement results. Instead of the conventional FFT analysis, a time-domain analysis is exploited for post-processing, from which a signature result can be obtained. This signature can distinguish between faulty devices and the fault-free devices. It is also used in the machine-learning-based test method to predict the dynamic specifications of the ADC. The experimental results of a 12-bit 80 M/s pipelined ADC are shown to evaluate the sensitivity and accuracy of using a pulse wave to test an ADC

Effective link operation duration: a new routing metric for mobile ad hoc networks

The dynamic topology of mobile ad hoc networks (MANETs) is caused by node mobility and fading of the wireless link. Link reliability is often measured by the estimated lifetime and the stability of a link. In this paper we propose that the stability of a link can be represented by the time duration in which the two nodes at each end of a link are within each other’s transmission range and the fading is above an acceptable threshold. A novel routing metric, called effective link operation duration (ELOD), is proposed and implemented into AODV (AODV-ELOD). Simulation results show that proposed AODVELOD outperforms both AODV and the Flow Oriented Routing Protocol (FORP)