HDL2GDS: a fully automated ASIC digital design flow
Bibliographic record
Abstract
HDL2GDS is a fully automated ASIC digital design flow capable of transforming VHDL or Verilog integrated circuit specifications into a corresponding GDSII mask layout file. With one command the RTL or behavioral-level HDL specification is synthesized, a simple floorplan is generated, blocks and macros are placed, power is routed, standard cells are placed, a clock-tree is generated, hold-time violations are detected and fixed, routing is performed, a manufacturing logo is added, IO pads are attached, the GDSII mask layout is exported and finally DRC & LVS and static timing analysis are performed. The flow incorporates the following tools, made available by the Canadian Microelectronics Corporation: Synopsys Design Compiler and PrimeTime, Cadence First Encounter and DFII, and Mentor Graphics Calibre. HDL2GDS is fully customizable with respect to capability and target libraries through the use of tool-scripts and a single design configuration file. The flow generates a GDSII mask layout for a 360 K gate communications chip within 30 hours on a Sun Fire V880 with 12 GB of memory. After much development effort the flow's ease of use is now comparable to FPGA synthesis. We discuss the limitations of the flow, the difficulties encountered when creating an automated digital design flow and maintenance challenges
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".