Regression Model-based VCO Design Optimization Technique

Hyun, Jin‐Won; Nam, Jae-Won

doi:10.1109/iceic57457.2023.10049908

Cited by 1 publication

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To enhance the design optimization process, our proposed zoom-in algorithm in this study achieves greater precision and efficiency compared to previous research efforts. While previous studies have explored the use of regression models for analog circuit design automation [3], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], the original zoom-in algorithm presented in [3] had a limitation in the accuracy of the regression model during the initial step due to a smaller amount of data compared to single-step approaches. To address this limitation, our study focuses on improving accuracy by incorporating the variational autoencoder (VAE) structure and refining the verification step within the algorithm.…”

Section: B Zoom-in Algorithmmentioning

confidence: 99%

“…The purpose of this research is to reduce the time required to automate the optimization design with Analog and Mixed-Signal (AMS) circuits and to obtain more accurate results. We implemented an algorithm for automatic circuit design utilizing the zoom-in algorithm proposed by Hyun and Nam [3], along with a regression model and a variational autoencoder structure. The proposed algorithm is applied to the automated design of a voltage-controlled oscillator (VCO) that consists of a ring oscillator circuit.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

AMS Circuit Design Optimization Technique Based on ANN Regression Model With VAE Structure

Hyun

Nam

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

The advanced design of an analog mixed-signal circuit is not simple enough to meet the requirements of the performance matrix as well as robust operations under process-voltage-temperature (PVT) changes. Even commercial products demand stringent specifications while maintaining the system's performance. The main objectives of this study are to increase the efficiency of the design optimization process by configuring the design process in multiple regression modeling stages, to characterize our target circuit into a regression model including PVT variations, and to enable a search for co-optimum design points while simultaneously checking performance sensitivity. We used an artificial neural network (ANN) to develop a regression model and divided the ANN modeling process into coarse and fine simulation steps. In addition, we applied a variational autoencoder (VAE) structure to the ANN model to reduce the training error due to an insufficient input sample. According to the proposed algorithm, the AMS circuit designer can quickly search for the co-optimum point, which results in the best performance, while the least sensitive operation as the design process uses a regression model instead of launching heavy SPICE simulations. In this study, a voltage-controlled oscillator (VCO) is selected to prove the proposed algorithm. Under various design conditions (CMOS 180 nm, 65 nm, and 45 nm processes), we proceed with the proposed design flow to obtain the best performance score that can be evaluated by a figure-of-merit (FoM). As a result, the proposed regression model-based design flow achieves twice accurate results in comparison to that of the conventional single-step design flow.INDEX TERMS Analog circuit design automation, variational-autoencoder, regression model, artificialneural-network, voltage-controlled-oscillator.

show abstract