TAFA: Design Automation of Analog Mixed-Signal FIR Filters Using Time Approximation Architecture

Su, Stephen Y. H.; Zhang, Qiaochu; Liu, Juzheng; Hassanpourghadi, Mohsen; Rasul, Rezwan A; Chen, Mike Shuo-Wei

doi:10.1109/asp-dac52403.2022.9712575

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…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%

“…Therefore, considering the PVT variations, optimization with simulation results over a wide range can design a circuit with better performance. Similarly, studies including corner simulation, layout generator, and Monte Carlo simulation have been developed to consider PVT variations [10], [11], [12], [13], [14]. These simulations to check PVT variations have the disadvantage of requiring a significant computer processing time.…”

Section: Reflecting Pvt Variationmentioning

confidence: 99%

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

Hyun

Nam

2023

IEEE Access

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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.

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