Computational Design of an Integrated CMOS Readout Circuit for Sensing With Organic Field-Effect Transistors
Bibliographic record
Abstract
Organic field-effect transistors (OFETs) are at the forefront of next generation electronics. This class of devices is particularly promising due to the possibility of fabrication on mechanically compliant and conformable substrates, and potential manufacturing at large scale through solution deposition techniques. However, their integration in circuits, especially using stretchable materials, is still challenging. In this work, the design and implementation of a novel structure for an integrated CMOS readout circuitry is presented and its fundamentals of operation are provided. Critical for sensing applications, the readout circuitry described is highly linear. Moreover, as several sources of mismatch and error are present in CMOS and OFET devices, a calibration technique is used to cancel out all the mismatches, thus delivering a reliable output. The readout circuit is verified in TSMC 0.18 μm CMOS technology. The maximum total power consumption in the proposed readout circuit is less than 571 μW, while fully loaded calibration circuit consumes a power less than 153 μW, making it suitable for sensors applications. Based on previously reported high mobility and stretchable semiconducting polymers, this new design and readout circuitry is an important step toward a broader utilization of OFETs and the design of stretchable sensors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".