A PCell design methodology for automatic layout generation of spiral inductor using skill script

Tayenjam, Sobhana; Kumaravel, S.

doi:10.1109/icmdcs.2017.8211572

Cited by 2 publications

(1 citation statement)

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“…SKILL-related works propose manually written SKILL programs that automate layout design. Tayenjam et al 13 propose a SKILL PCell to automatically generate inductor layouts given certain input parameters. Abhishek et al 14 propose an algorithm written in SKILL to optimize inductor layouts toward minimizing loss factors.…”

Section: Related Workmentioning

confidence: 99%

A machine learning approach towards SKILL code autocompletion

Dehaerne,

Dey,

Meert

et al. 2024

DTCO and Computational Patterning III

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As Moore's Law continues to increase the complexity of electronic systems, Electronic Design Automation (EDA) must advance to meet global demand. An important example of an EDA technology is SKILL, a scripting language used to customize and extend EDA software. Recently, code generation models using the transformer architecture have achieved impressive results in academic settings and have even been used in commercial developer tools to improve developer productivity. To the best of our knowledge, this study is the first to apply transformers to SKILL code autocompletion towards improving the productivity of hardware design engineers. In this study, a novel, data-efficient methodology for generating SKILL code is proposed and experimentally validated. More specifically, we propose a novel methodology for (i) creating a high-quality SKILL dataset with both unlabeled and labeled data, (ii) a training strategy where T5 models pre-trained on general programming language code are fine-tuned on our custom SKILL dataset using self-supervised and supervised learning, and (iii) evaluating synthesized SKILL code. We show that models trained using the proposed methodology outperform baselines in terms of human-judgment score and BLEU score. A major challenge faced was the extremely small amount of available SKILL code data that can be used to train a transformer model to generate SKILL code. Despite our validated improvements, the extremely small dataset available to us was still not enough to train a model that can reliably autocomplete SKILL code. We discuss this and other limitations as well as future work that could address these limitations.

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Section: Related Workmentioning

confidence: 99%