Related Experiment Video
Updated: Mar 3, 2026

12:19
Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source
Published on: April 4, 2017
8.9K
Performance prediction for silicon photonics integrated circuits with layout-dependent correlated manufacturing
Optics Express
|May 5, 2017
Summary
This study introduces an enhanced Monte Carlo (MC) simulation to predict how manufacturing variations affect photonics integrated circuits (PICs). The method accurately models layout-dependent variations, improving performance predictions for these complex optical devices.
Area of Science:
- Photonics
- Integrated Circuit Design
- Manufacturing Process Control
Background:
- Manufacturing variations significantly impact the performance of photonics integrated circuits (PICs).
- Accurate prediction of these variations is crucial for reliable circuit design and fabrication.
- Existing simulation methods often struggle to capture layout-dependent correlated variations.
Purpose of the Study:
- To develop an enhanced Monte Carlo (MC) simulation methodology for predicting the impact of layout-dependent correlated manufacturing variations on PIC performance.
- To establish a sub-nanometer accurate method for characterizing photonics manufacturing variations.
- To enable circuit simulations that account for correlated variations between components.
Main Methods:
- Characterization of waveguide width and height variations using spectral response of racetrack resonators.
- Development of models for layout-dependent enhanced MC simulation using netlist extraction.
- Simulation of spatially correlated physical variations across PICs on a discrete grid and mapping to circuit components.
Main Results:
- Demonstrated a sub-nanometer accurate method to characterize photonics manufacturing variations.
- Developed and detailed the simulation flow and theoretical models for layout-dependent enhanced MC simulation.
- Successfully predicted common-mode and differential-mode variations in ring-resonator filter circuits using the enhanced MC simulation.
Conclusions:
- The enhanced MC simulation methodology effectively predicts the performance impacts of layout-dependent correlated manufacturing variations in PICs.
- The developed characterization method provides high accuracy for extracting waveguide dimensions.
- This approach offers a powerful tool for improving the design and reliability of integrated photonics circuits.
Related Concept Videos
Propagation of Uncertainty from Systematic Error
1.5K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.5K
Propagation of Uncertainty from Random Error
2.0K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
2.0K
Prediction Intervals
3.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.5K
Semiconductors
1.7K
There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...
1.7K
Biasing of Metal-Semiconductor Junctions
717
Biasing metal-semiconductor junctions involves applying a voltage across the junction. Specifically, the metal is connected to a voltage source, while the semiconductor is grounded. This technique is essential for controlling the direction and magnitude of current flow in electronic devices, including diodes, transistors, and photovoltaic cells.
In Schottky junctions, where the semiconductor is n-type, applying a positive voltage to the metal relative to the semiconductor reduces its Fermi...
In Schottky junctions, where the semiconductor is n-type, applying a positive voltage to the metal relative to the semiconductor reduces its Fermi...
717
Poisson Probability Distribution
12.2K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
The...
12.2K

