Automatic Generation Strategy for Standard Cell Layout in DTCO Process Based on Reinforcement Learning
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe manuscript proposes the use of reinforcement learning (RL) for layout placement and the Dijkstra algorithm for routing is novel. The proposed method tries to bridge the gap between design rule constraints and adaptability to process node variations, a critical need in modern semiconductor design. While the idea is interesting there are several shortcoming that need to be addressed. More specifically:
- While the manuscript effectively cites prior works, it fails to provide quantitative comparisons with recent advancements in automatic layout design to highlight specific improvements.
- The Q-learning algorithm is not explained in details. Authors should provide detailed analysis regarding the design of the reward function in the reinforcement learning framework could benefit from a more in-depth explanation. For instance, how different weightings affect the layout performance is not fully explored.
- While the Dijkstra algorithm is used for routing, the manuscript does not adequately address challenges like congestion or multi-layer routing in dense designs.
- Authors should provide a complexity analysis of their proposed approach. Computational complexity of the proposed algorithms should be analysed for its scalability to larger, more complex standard cells.
- The paper does not provide a detailed analysis of scenarios where the method might fail or produce suboptimal results.
- The manuscript does not detail the computational resources required for training and execution of the proposed algorithms. For industrial adoption, resource efficiency is critical.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis manuscript introduces a method for automatically generating standard cells using reinforcement learning for placement and a Dijkstra algorithm for routing.
These methods address the challenges of complex Design Rule Checking (DRC) caused by uncoordinated standard cell placement and routing.
Standard cells are fundamental components of digital Very Large Scale Integration (VLSI) design, with modern designs comprising hundreds of millions of standard cell instances. Design Technology Co-Optimization (DTCO) allows for simultaneous optimization of standard cells and chip designs, improving performance.
Strength:
1. The proposed techniques can help semiconductor fabrication facilities reduce costs and shorten time to market during advanced process development.
2. The authors described their proposed method clearly with the help of a diagram.
Request on Areas for Improvement:
(1) The authors are requested to demonstrate how the layout generated by the proposed technique is comparable to the industry-standard cell library in terms of area, power, etc.
(2) The authors compared the performance of the proposed automatically generated layout cell to that of the manual technique. For comparison purposes, a few Design Technology Co-Optimization techniques are requested.
For example, the following paper uses a Reinforcement learning technique to implement DTCO. The authors can use this for comparison purposes.
H. Ren and M. Fojtik, "Invited- NVCell: Standard Cell Layout in Advanced Technology Nodes with Reinforcement Learning," 2021 58th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 2021, pp. 1291-1294, doi: 10.1109/DAC18074.2021.9586188.
(3) The authors asserted that the proposed technique can solve the DRC problem. Please show a layout with DRC error and a layout where DRC is fixed using the proposed method.
(4) The authors are requested to add references that used reinforcement techniques for automatic layout design or chip design.
For example:
A.Mirhoseini and et al, “Chip placement with deep reinforcement learning,” 2020. [Online]. Available: https://arxiv.org/abs/2004.10746
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsThe paper proposes a novel approach leveraging Q-learning and the Dijkstra algorithm for the automatic generation of standard cell layouts, claiming significant time savings and performance improvements in the Design-Technology Co-Optimization (DTCO) process.
1. The proposed algorithm was evaluated on 55nm and 28nm process nodes. While the results are clearly presented, additional case studies on other process nodes would enhance the generalizability and robustness of the proposed method.
2. Although the use of Q-learning and the Dijkstra algorithm is well-documented, the explanations may be challenging for non-expert or beginner readers to grasp. Including more intuitive descriptions or supplementary diagrams of the core algorithms would improve the accessibility of the paper.
3. While the paper focuses on the automation of standard cell layout in the DTCO process, it lacks a discussion on the practical integration of the proposed methodology into industrial EDA (Electronic Design Automation) tools. Providing clearer guidance on the applicability and potential limitations in real-world scenarios would add significant value.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe authors have addressed all the concerns improving the quality of the manuscript.
Reviewer 2 Report
Comments and Suggestions for AuthorsThe revised manuscript addresses all the concerns. Table 1 now includes comparison information between the manually designed industry-standard cell layout and the automatically generated standard cell layout using the proposed method. The relevant suggested references have also been added to the manuscript. The revised manuscript also provides a layout with DRC errors and a DRC-free layout using the proposed method. Overall, the paper's quality has improved, and it presents a valuable contribution to the literature.
