Contributions and Legacy of David C. Joy to Monte Carlo Simulations in Electron and Ion Microscopy
Bibliographic record
Abstract
David C. Joy was a great microscopist, pioneer, educator, generous of his time to give technical advice and help, and a great mentor for many of us. David’s contributions to the microscopy and microanalysis community are enormous and cover many topics. Monte Carlo simulations is one of them in which David had very important contributions and a legacy that affect directly and indirectly a vast amount of microscopist. David’s adventure in Monte Carlo electron trajectory simulation starts when he builds a practical Monte Carlo program to help SEM image interpretation for semiconductor metrology at the Bell Labs. Later, he develops and applied Monte Carlo simulations for various applications in electron microscopy and microanalysis. One of his most important contributions was the book “Monte Carlo modeling for electron microscopy and microanalysis”. This book is a practical guide how to program and use Monte Carlo simulations with examples of complete code [1]. Each chapter shows a different approach or application like backscattered electron yield, charge collection microscopy and cathodoluminescence, secondary electron yields and imaging, and X-ray production and microanalysis. Experimental data are needed to validate Monte Carlo simulation programs. To help the development of such programs, David builds a database on electron-solid interactions from published measurements of backscattering yields, secondary electron yields, stopping powers, and X-ray ionization cross sections [2]. This database was freely available on David’s webpage and updated continuously. Figure 2 shows an example of comparison from experimental data of the database with prediction from the CASINO version 3 Monte Carlo program. For an accurate Monte Carlo program, a better model for the elastic cross section than the Rutherford analytic model is needed like the Mott model. However, the Mott’s model is not represented by analytical equations, but by numeric tables that need to be calculated. David and his collaborators have calculated such tables for the Mott elastic cross section for most elements and the energy range 20 eV to 20 keV [3]. More importantly, David had made these tables available to the researcher community, which was used to develop popular Monte Carlo programs like CASINO, WinX-Ray, and MC-Xray. David was an early adopter and supporter of the Helium ion microscope (HIM) which had a better spatial resolution than a SEM. To better understand the ion-solid interaction and the image formation, he collaborated to develop a Monte Carlo simulation of secondary electrons for the HIM [4]. This IONiSE Monte Carlo program correctly predicts the variation of the ion induced secondary electron (iSE) yield with Helium energy and target material. Some of David Joy important publications [1–4] on Monte Carlo simulations of electron and ion trajectories in matter and an example of depth colored electron trajectories from the IONiSE Monte Carlo simulation program. An example of Monte Carlo program validation using Joy’s database of experimental data for Silicon.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.020 |
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".