Invited: Mapping Two Decades of Innovation: Lessons from 25 Years of ISPD Research
Bibliographic record
Abstract
The design automation research community has driven the evolution of integrated circuits from a handful of transistors in the 1960s to billions today. The International Symposium on Physical Design (ISPD) has been instrumental in tackling challenges like scaling complexities, hardware security, and the exponential growth in transistor counts. This study conducts a comprehensive bibliometric analysis of ISPD publications using Natural Language Processing, machine learning, and network analysis. It explores research themes, collaboration dynamics, and global contributions through citation networks, co-authorship graphs, geographical and spatial mapping, and topic modeling. Key areas of focus include Physical Design Optimization, Power Efficiency, and Emerging Technologies, with prominent topics such as placement, routing, clock skew, lithography, machine learning, and hardware security. The analysis highlights the evolution of foundational techniques like placement and routing while identifying emerging trends such as AI-driven design automation. These insights provide a roadmap for sustaining innovation in physical design over the next 25 years.
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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".