Post Silicon Characterization of Digital to Analog Converter using Arduino Uno
Bibliographic record
Abstract
This paper explains the characterization process for a DAC (Digital to Analog Converter). Parameters like DNL, INL, gain error, offset error, and their characterization processes have been discussed in detail. A remarkable change has struck the semiconductor industry where open-source Electronic Design Automation tools are being advertised to encourage people towards chip design. Tools like Open Lane support RTL to GDS flow. Companies like Google are facilitating free tape-outs of designs built using opensource PDKs like Sky Water 130 nm PDK. When the ICs are manufactured in large numbers, the fabrication process is not uniform hence, it causes variations in device behavior. Process variations may result in faulty semiconductors that can only be detected when they are tested after the tape out is done. In this paper, characterization of a DAC IC (MCP4921) has been performed without the use of an industry level characterization board and industry-level chip programmer. Arduino Uno which is an open-source micro controller has been used to program the IC by establishing an SPI interface with the DAC IC and the parameters have been measured using instruments like oscilloscope and source measure units which have been automated using Python. Python is also used to store the set of data obtained from the IC and this data is later postprocessed to calculate DC parameters like DNL, INL and Gain Error.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".