Ultrahigh-Sensitive CMOS pH Sensor Developed in the BEOL of Standard 28 nm UTBB FDSOI
Bibliographic record
Abstract
This paper reports ultrahigh-sensitive and ultralow-power CMOS compatible pH sensors that are developed in the back-end-of-line (BEOL) of industrial 28-nm ultrathin body and buried oxide (UTBB) fully depleted silicon-on-insulator (FDSOI) transistors. Fabricating the sensing gate and the control gate of the sensors in a capacitive divider circuit, CMOS compatible pH sensors are demonstrated where the front gate bias is applied through a control gate rather than a bulky reference electrode. On the other hand, the strong electrostatic coupling between the front gate and the back gate of FDSOI devices provide an intrinsic signal amplification feature for sensing applications. Utilizing an atomic layer deposited aluminum oxide (Al <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) as a pH sensing film, pH sensors having a sensitivity of 475 mV/pH and 730 mV/pH in the extended gate and BEOL configuration, respectively, are reported. Sensitivities of both configurations are superior to state-of-the-art low power ion-sensitive field-effect transistors. The small sensing area and the FDSOI-based low power technology of the device make the sensors ideal for the IoT market. The proposed approach has been validated by TCAD simulation, and demonstrated through experimental measurements on proof-of-concept extended gate pH sensors and on sensors that are developed in the BEOL of industrial UTBB FDSOI devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".