A 65-nm CMOS Self-Supplied Power Management System for Near-Field Wirelessly Powered Biomedical Devices
Bibliographic record
Abstract
This paper proposes a self-supplied power management system to efficiently rectify and regulate the AC voltage received from wireless power transmission techniques to power or recharge biomedical devices. The proposed power management system comprises three integrated functional units, namely, a fully cross-coupled rectifier, a self-biased reference voltage, and a capacitor-less low-dropout regulator (LDO). To reduce the current complexity of designing capacitor-less LDOs, a new architecture based on a pair of diode-connected transistors at the load of the LDO is devised which alleviates the need for a large load capacitor. The proposed power management system is implemented in a 65-nm CMOS process with an active chip area of 0.0810 mm2. Experimental results indicate that this system is capable of rectifying an AC signal up to 5 V at a frequency of 6.78 MHz. This rectified signal is then regulated to a fixed DC voltage of 1.75 V, while the load current can vary between 0 and 75 mA, with a maximum voltage dropout of 170 mV. Advantageously, the proposed power management system is significantly robust to temperature, as a 55 °C change in ambient temperature leads to only a 9% degradation in its overall performance. Furthermore, the ability of the power management system to drive low-power consumer electronics is demonstrated, and its superiority is evidenced by a performance comparison with the latest integrated power management systems presented in the literature.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".