Bibliographic record
Abstract
The Shastri lab focuses on generating advanced photonic chips for signal processing and computing by combining artificial intelligence (AI) and photonics. These chips are utilized in neuromorphic silicon photonics which has various applications such as improving computational efficiency in AI and neuromorphic computing hardware. One of our advanced chips can be divided into three physical components: receiving a light signal, modulating the signal, and lastly detecting the signal with a photodetector on chip. Prior to utilizing these chips for experiments, it is vital to ensure that all components are functioning correctly. My work focused on streamlining the testing process of the photodetector by improving the signals used within the process. In order to test the photodetector, the light entering must be modulated externally using a Mach-Zehner Modulator (MZM). The MZM takes in a light signal and splits it into two where they experience a phase shift and when the two are recombined create a modulated signal. The signal’s modulation is determined by changing the radio frequency (RF) signal sent from a driver into the modulator. Another aspect of my work was enhancing the control of the RF signal produced by the driver. The driver requires specific positive and negative voltages to generate ideal frequencies which are supplied by a unique power source. The voltage source was designed to ensure that the driver never received a damaging current or voltage and had a user-friendly interface to control the modulation of the signal.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".