Mitigating Phase Error Accumulation in Programming MZI-Based Optical Processors
Bibliographic record
Abstract
The practical implementation of reconfigurable interferometric-based optical processors requires complicated and time-consuming calibration and programming schemes to address errors induced by fabrication process variations. Among the various prevalent mesh topologies, the presence of a diagonal path inherently provides independent access to calibrate, monitor, and program each phase shifter, regardless of any existing dynamic errors in the bias of other blocks. In other words, the phase error of MZIs do not accumulate along a diagonal path. This attribute of the diagonal path has been experimentally validated in a 4×4 Bokun mesh topology by introducing phase distortion in the previous Mach-Zehnder interferometer block along the same diagonal path. Measurements demonstrate that the same phase distortion in a mesh topology lacking this attribute can lead to accumulating calibration and programming errors. The Bokun mesh topology, with its inherent beneficial attributes, supports the realization of significantly superior performance in the case of on-chip weight optimization, referred to as in-situ programming, due to enabling an error-free, faster, and easier programming scheme.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".