Bibliographic record
Abstract
This tutorial will look at the fundamental methods of digital sampling and how to apply it to sigma-delta modulators and other sigma-delta based devices. We begin by describing the principles of digital sampling and how one extends this theory to the test of sigma-delta based data converters and related devices. A review of the basic ideas of coherent testing will be given, such as the application of the M/N coherency principle for performing gain, frequency and distortion type measurements. The impact of clock jitter will also be emphasized. Next, we'll extend the sampling principle to the non-coherent test situation and describe how one performs measurements of deterministic and random signals using a pre-processing step involving windowing. Subsequently, the concept of a periodogram will be introduced and shown how it is used to estimate the power spectral density of a random signal (i.e., noise). At this point in the discussion we'll review the basic ideas behind sigma-delta modulators and their application to data conversion. We'll look at lowpass and bandpass type modulators, as well as single-loop, multi-loop multi-stage, continuous-time and sampled-data implementations. The goal is to expose the students to the underlying principles behind new IC developments and trends, rather than expose the students to detail design issues. At this point, specific issues related to estimating the power spectral density of a sigma-delta modulator using a periodogram will be described. The remainder of the tutorial will look at different ways in which sigma-delta techniques can be used for Design-For-Test. One section will describe different methods in which to generate high-precision analog signals, such as DC, sinusoids, multi-tones, Gaussian noise signals, phase and frequency modulated signals, etc. Such methods have application for retrofitting digital testers as mixed-signal testers, as well as extending the capability of existing testers. Subsequently, we'll demonstrate how sigma-delta methods can be used in a wide range of DFT/BIST circuits for SOC applications. This will include signal sources, digitizers, coherent samplers, time-domain reflectometry and transmission, and noise and jitter analyzers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".