Expert Commentary: Automation and Knowledge Graph for High-Performance Analog and Mixed-Signal Circuit Design
In the field of Electronic Design Automation (EDA), the design of high-performance analog and mixed-signal (AMS) circuits has traditionally been a time-consuming and labor-intensive process. The design of these circuits has heavily relied on the experience and expertise of designers, making automation a significant challenge. However, recent advancements in large language models (LLMs) have provided a new avenue for automating the design process of AMS circuits.
The main issue faced by LLMs in AMS circuit design is the lack of high-quality datasets. This limitation has resulted in model hallucination, where the generated circuit designs lack robustness and fail to meet the desired specifications. To address this issue, the paper introduces AMSnet-KG, a dataset that includes various AMS circuit schematics and netlists. This dataset not only provides a large and diverse collection of circuit designs but also includes annotations on their functional and performance characteristics. These annotations are crucial for training LLMs to generate circuit designs that are both functional and meet the required performance metrics.
Using AMSnet-KG as a foundation, the paper proposes an automated AMS circuit generation framework that leverages the knowledge embedded in LLMs. The framework follows a systematic design strategy where the circuit architecture is formulated based on required specifications. Components that match these specifications are then retrieved from the dataset and assembled into a complete circuit topology. Bayesian optimization is employed to determine the optimal transistor sizing for the circuit.
After the initial design is generated, simulation results are fed back into the LLM to refine the topology further. This iterative process ensures that the circuit meets the desired specifications and performance metrics. The utilization of LLMs not only accelerates the design process but also reduces the need for extensive manual intervention, resulting in more efficient and robust circuit designs.
The paper’s case studies on operational amplifier and comparator design demonstrate the effectiveness of the automated design flow. By inputting the required specifications, the framework generates netlists for these circuits with minimal human effort. This showcases the potential of the proposed approach in automating complex AMS circuit design tasks.
Overall, the introduction of AMSnet-KG and the use of LLMs in the automated design flow hold promise for the future of high-performance analog and mixed-signal circuit design. The availability of a high-quality dataset and leveraging the power of LLMs can greatly enhance the efficiency and robustness of the design process, ultimately leading to better circuit performance and reduced design time. The open-sourcing of the dataset will further contribute to the growth of this research field and foster collaboration among researchers and designers.