Rapid FinFET Modelling Using an Autoencoder

• URL: http://arxiv.org/abs/2606.24046v1
• Date: Tue, 23 Jun 2026 01:15:50 GMT
• Title: Rapid FinFET Modelling Using an Autoencoder
• Abstract summary: We train an autoencoder that compresses full I-V curves into a low-dimensional latent space.<n>A key innovation is the explicit incorporation of parameter such as drain to source voltage (VDS) as an input feature.<n>The trained model successfully reconstructs full I-V curves and directly extracts critical device metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents a machine learning framework that leverages an autoencoder (AE) for the efficient modeling of FinFET. We first calibrated a BSIM-CMG model to generate a dataset of current-voltage (ID-VG) characteristics. This data was used to train an autoencoder that compresses full I-V curves into a low-dimensional latent space, which intrinsically encodes key device physics. A key innovation is the explicit incorporation of parameter such as drain to source voltage (VDS) as an input feature, enhancing the model ability to capture bias dependent variation. The trained model successfully reconstructs full I-V curves and directly extracts critical device metrics including threshold voltage (VTH), subthreshold slope (SS), and peak transconductance (gm). This approach demonstrates that data driven compact models, built from actual characterization data, can achieve high accuracy with minimal training data, providing a powerful tool for rapid device characterization, modelling and circuit level simulation.

Related papers

• AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries [47.27974914502511]
  We introduce a deep learning surrogate pipeline based on the Swin3D Transformer to predict discharge dynamics directly from data.<n>The proposed method reduces the computational overhead by orders of magnitude, providing a scalable and efficient framework for high- throughput battery design and optimization.
  arXiv  Detail & Related papers  (2026-07-22T09:10:48Z)
• Fast Data-Driven Modeling of Hydraulic Clutch Control Pressure with Latch-State Classification and Gaussian Process Regression [0.0]
  This paper presents a data-driven method for modeling the pressure response of a hydraulic clutch control circuit.<n>The proposed approach was evaluated on unseen ramp-rate data and compared against a physics-based Amesim model.
  arXiv  Detail & Related papers  (2026-07-11T21:07:02Z)
• Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training [46.75046795995564]
  We present the first theoretical characterization of the pulse complexity of SP calibration and the resulting estimation error.<n>We propose a dynamic SP estimation method that tracks the SP during model training, and establishes its convergence guarantees.
  arXiv  Detail & Related papers  (2026-02-24T19:41:34Z)
• Fractional-order Modeling for Nonlinear Soft Actuators via Particle Swarm Optimization [49.34452620841728]
  This paper proposes an innovative modeling framework based on fractional-order differential equations (FODEs) to capture the dynamic behavior of soft materials.<n>The proposed approach effectively represents the complex deformation phenomena inherent in soft actuators.
  arXiv  Detail & Related papers  (2025-12-20T04:46:25Z)
• An Interpretable Operator-Learning Model for Electric Field Profile Reconstruction in Discharges Based on the EFISH Method [1.8159327527122784]
  This study introduces a novel machine learning model with markedly improved performance.<n>Termed Decoder-DeepONet (DDON), its main strength is learning function-to-function mappings.<n>In almost all cases, the DDON model exhibits better generalizability, higher prediction accuracy, and wider applicability.
  arXiv  Detail & Related papers  (2025-11-29T07:05:07Z)
• SEMPO: Lightweight Foundation Models for Time Series Forecasting [45.456949943052116]
  SEMPO is a lightweight foundation model that requires pretraining on relatively small-scale data, yet exhibits strong general time series forecasting.<n> SEMPO comprises two key modules: 1) energy-aware SpEctral decomposition module, that substantially improves the utilization of pre-training data.<n>Experiments on two large-scale benchmarks covering 16 datasets demonstrate the superior performance of SEMPO in both zero-shot and few-shot forecasting scenarios.
  arXiv  Detail & Related papers  (2025-10-22T15:58:44Z)
• PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders [42.8983261737774]
  We propose a novel generative model that learns from limited data by incorporating physical constraints to enhance performance.<n>We extend the VAE architecture by incorporating physical models in the generative process, enabling it to capture underlying dynamics more effectively.<n>We demonstrate that PIGPVAE can produce realistic samples beyond the observed distribution, highlighting its robustness and usefulness under distribution shifts.
  arXiv  Detail & Related papers  (2025-05-25T21:12:01Z)
• Drivetrain simulation using variational autoencoders [0.0]
  This work proposes variational autoencoders (VAEs) to predict a vehicle's jerk signals from torque demand.<n>We implement both unconditional and conditional VAEs, trained on experimental data from two variants of a fully electric SUV.
  arXiv  Detail & Related papers  (2025-01-29T13:37:32Z)
• Stroke classification using Virtual Hybrid Edge Detection from in silico electrical impedance tomography data [37.94947316140192]
  Electrical impedance tomography (EIT) is a non-invasive imaging method for recovering the internal conductivity of a physical body.<n>Most previous works have used raw EIT voltage data as network inputs.<n>We build upon a recent development which suggested the use of special noise-robust Virtual Hybrid Edge Detection (VHED) functions as network inputs.
  arXiv  Detail & Related papers  (2025-01-24T18:29:34Z)
• Event-Based Simulation of Stochastic Memristive Devices for Neuromorphic Computing [41.66366715982197]
  We build a general model of memristors suitable for the simulation of event-based systems. We extend an existing general model of memristors to an event-driven setting. We demonstrate an approach for fitting the parameters of the event-based model to the drift model.
  arXiv  Detail & Related papers  (2024-06-14T13:17:19Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Physics-informed CoKriging model of a redox flow battery [68.8204255655161]
  Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently. There is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance. We develop a multifidelity model for predicting the charge-discharge curve of a RFB.
  arXiv  Detail & Related papers  (2021-06-17T00:49:55Z)
• Robust Data-Driven Error Compensation for a Battery Model [0.0]
  Today's massively collected battery data is not yet used for more accurate and reliable simulations. A data-driven error model is introduced enhancing an existing physically motivated model. A neural network compensates the existing dynamic error and is further limited based on a description of the underlying data.
  arXiv  Detail & Related papers  (2020-12-31T16:11:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.