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Time-based analog-to-digital converters
- "We also extensively analyze the fundamental performance limitation of the spiking neuron TB-ADC due to nonidealities such as frequency aliasing, leaky integration, thermal noise, signal-dependent threshold variation, and time jitter. Much of this analysis can be extended to other TB-ADC implementations. We also discuss some other TB-ADC variations including clocked neurons and level-mode TB-ADCs. Test results of several prototype chips implemented in 0.5 um CMOS technology process suggest that high resolution and low power consumption TB-ADCs are achievable in practice."
- "ABSTRACT: We present the concept and some implementations of time-based analog-to-digital converters (TB-ADCs). The TB-ADC employs a fundamentally different architecture from the conventional ADC and achieves data conversion by representing signals as a series of discrete time events. This novel architecture trades off simpler analog circuitry on the front end for more complex digital circuitry on the back end, and is very promising for low power applications. We show that theoretically we can use the weighted low-pass kernel (WLPK) method to perfectly reconstruct the signal for the TB-ADCs. This method can also be extended to solve general nonuniform sampling problems. We investigate the effect of different low-pass kernels such as Sinc and Gaussian on the reconstruction performance and computation cost."
- "Time-based analog-to-digital converters"