DSP-Based Testing of Analog and Mixed-Signal Circuits
DSP-Based Testing of Analog and Mixed-Signal Circuits
Analog Signal Generation for Built-in-Self-Test of Mixed-Signal Integrated Circuits
Analog Signal Generation for Built-in-Self-Test of Mixed-Signal Integrated Circuits
A Signature Analyzer for Analog and Mixed-signal Circuits
ICCS '94 Proceedings of the1994 IEEE International Conference on Computer Design: VLSI in Computer & Processors
HABIST: Histogram-Based Analog Built-In Self-Test
Proceedings of the IEEE International Test Conference
A Simplified Polynomial-Fitting Algorithm for DAC and ADC BIST
Proceedings of the IEEE International Test Conference
A Built-in Self- Test for ADC and DAC in a Single-Chip Speech CODEC
Proceedings of the IEEE International Test Conference on Designing, Testing, and Diagnostics - Join Them
Efficient and accurate testing of analog-to-digital converters using oscillation-test method
EDTC '97 Proceedings of the 1997 European conference on Design and Test
Built-in self-test methodology for A/D converters
EDTC '97 Proceedings of the 1997 European conference on Design and Test
Hardware Resource Minimization for Histogram-Based ADC BIST
VTS '00 Proceedings of the 18th IEEE VLSI Test Symposium
A BIST Scheme for SNDR Testing of ΣΔ ADCs Using Sine-Wave Fitting
Journal of Electronic Testing: Theory and Applications
An Output Response Analyzer Circuit for ADC Built-in Self-Test
Journal of Electronic Testing: Theory and Applications
| Hi-index | 0.00 |
The histogram method is a very classical test technique for Analog to Digital Converters (ADCs), but only used for external testing because of the large amount of required hardware resources. This paper discusses the viability of a BIST implementation for this technique. An original approach is developed that permits to extract the ADC parameters with a reduced area overhead. This approach involves (i) the calculation of the parameters using approximations and (ii) the decomposition of the global test in a code-after-code test procedure. These two features allow a significant reduction of the required operative resources and memory dedicated to the storage of experimental data. In addition, the use of a piece-wise approximation for computing the ideal histogram also permits to minimize the memory dedicated to the storage of reference data.