Mixed-Variable Bayesian Optimization for Analog Circuit Sizing through Device Representation Learning

Touloupas, Konstantinos; Sotiriadis, Paul P.

doi:10.3390/electronics11193127

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…For this reason VAEs are widely used as generative models. While their original application was generating artificial faces, they are used in other fields like text processing [17], generating audio signals [18], creating level maps for video games [19] or mapping high-dimensional search spaces into lower-dimensional ones in order to facilitate optimization [20]. However, no information regarding the use of VAEs in semiconductor-device modeling is to be found in the literature.…”

Section: Autoencoders and Variational Autoencoders: Theoretical Backg...mentioning

confidence: 99%

Generative Modeling of Semiconductor Devices for Statistical Circuit Simulation

Kasprowicz,

Kasprowicz

2024

Preprint

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Emerging semiconductor devices often lack accurate analytical models. The same is usually true of any devices working under extreme conditions like cryogenic temperatures. The usual workaround involves the use of approximation models, usually based on lookup tables or neural networks individually fitted to measurement data. In the case of experimental devices or ones working under extreme conditions, the number of units available for measurement is limited. As a result, the number of approximation-model instances is too small to enable a statistical simulation of even middle-sized circuits, which is a necessary step in integrated-circuit design since it provides the realistic picture of the circuit’s behavior in the presence of manufacturing process variations. Approximation models using structure parameters as inputs do exist in the literature, but are only useful if the end user knows the statistical distributions of those parameters, which is not usually the case. We propose a technique based on generative machine learning, namely the variational autoencoder, that uses only a small sample of devices to capture the essential features of their I–V curves under process variations and subsequently generates an arbitrary number of similarly disturbed curves. The model trained on as few as 20 instances per device type is shown to precisely reproduce the distributions of period and power consumption of a ring oscillator.

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Section: Autoencoders and Variational Autoencoders: Theoretical Backg...mentioning

confidence: 99%

Generative Modeling of Semiconductor Devices for Statistical Circuit Simulation

Kasprowicz,

Kasprowicz

2024

Preprint

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show abstract

“…For this reason, VAEs are widely used as generative models. While their original application was generating artificial faces, they are used in other fields like text processing [18], generating audio signals [19], creating level maps for video games [20], or mapping high-dimensional search spaces into lower-dimensional ones in order to facilitate optimization [21]. However, no information regarding the use of VAEs in semiconductor device modeling has been reported in the literature.…”

Section: Autoencoders and Variational Autoencoders: Theoretical Backg...mentioning

confidence: 99%

Generative Modeling of Semiconductor Devices for Statistical Circuit Simulation

Kasprowicz,

Kasprowicz

2024

Electronics

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Statistical simulation is a necessary step in integrated circuit design since it provides a realistic picture of the circuit’s behavior in the presence of manufacturing process variations. When some of the circuit components lack an accurate analytical model, as is often the case for emerging semiconductor devices or ones working at cryogenic temperatures, an approximation model is necessary. Such models are usually based on a lookup table or artificial neural network individually fitted to measurement data. If the number of devices available for measurement is limited, so is the number of approximation model instances, which renders impossible a reliable statistical circuit simulation. Approximation models using the device’s physical parameters as inputs have been reported in the literature but are only useful if the end user knows the statistical distributions of those parameters, which is not always the case. The solution proposed in this work uses a type of artificial neural network called the variational autoencoder that, when exposed to a small sample of I-V curves under process variations, captures their essential features and subsequently generates an arbitrary number of similarly disturbed curves. No knowledge of the underlying physical sources of these variations is required. The proposed generative model trained on as few as 20 instances of a MOSFET is shown to precisely reproduce the period and power consumption distributions of a ring oscillator built with these MOSFETs.

show abstract

Mixed-Variable Bayesian Optimization for Analog Circuit Sizing through Device Representation Learning

Cited by 2 publications

References 23 publications

Generative Modeling of Semiconductor Devices for Statistical Circuit Simulation

Generative Modeling of Semiconductor Devices for Statistical Circuit Simulation

Generative Modeling of Semiconductor Devices for Statistical Circuit Simulation

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