RRAM Compact Modeling Using Physics and Machine Learning Hybridization

Lin, Albert; Liu, Po-Ning; Yang, Zheng-Kai; Rawat, Tejender Singh; Tseng, Tseung‐Yuen

doi:10.1109/ted.2022.3152978

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…Inspired by the two-module framework of existing RRAM models as shown in Figure 2, the PiRNN consists of an RNN module to model the internal state evolution associated to a given input voltage sequence, and an MLP module to generate the output current based on the internal state and the inputs. The RNN module, corresponding to the state module, takes the current voltage V, the previous voltage V pre [27], and the device width W as the primary inputs and outputs the hidden state h, which is an m-bit vector. The hidden state is then fed back to form another input to the RNN, which is a critical operation that handles time-series data or the memory effect of RRAM devices in our application scenario.…”

Section: Basic Structure Of Pirnnmentioning

confidence: 99%

A Physics-Informed Recurrent Neural Network for RRAM Modeling

Sha

Lan

et al. 2023

Electronics

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Extracting behavioral models of RRAM devices is challenging due to their unique “memory” behaviors and rapid developments, for which well-established modeling frameworks and systematic parameter extraction processes are not available. In this work, we propose a physics-informed recurrent neural network (PiRNN) methodology to generate behavioral models of RRAM devices from practical measurement/simulation data. The proposed framework can faithfully capture the evolution of internal state and its impacts on the output. A series of modifications informed by the RRAM device physics are proposed to enhance the modeling capabilities. The integration strategy of Verilog-A equivalent circuits, is also developed for compatibility with existing general-purpose circuit simulators. The Verilog-A model can be easily adopted into the SPICE-type simulator for the circuit design with a variable step that differs from the training process. Numerical experiments with real RRAM devices data demonstrate the feasibility and advantages of the proposed methodology.

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Section: Basic Structure Of Pirnnmentioning

confidence: 99%

A Physics-Informed Recurrent Neural Network for RRAM Modeling

Sha

Lan

et al. 2023

Electronics

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“…This approach is very general, meaning that the MLP architecture itself will remain relatively constant between different devices. Another approach commonly used is physics-inspired neural network models [11,12]. These physics-inspired models modify the standard, sequential MLP architecture to match more closely to the known underlying physics of the device.…”

Section: Introductionmentioning

confidence: 99%

“…One approach that has been proposed for creating machine learning-based compact models for multistate devices is to use long short-term memory (LSTM) neural networks [13]. As pointed out in a later work by the same group, LSTMs are far too computationally expensive for compact models [11]. As a result, they chose to use a physics-hybridization approach to create compact models for resistive random-access memory (RRAM) [11].…”

Section: Introductionmentioning

confidence: 99%

“…As pointed out in a later work by the same group, LSTMs are far too computationally expensive for compact models [11]. As a result, they chose to use a physics-hybridization approach to create compact models for resistive random-access memory (RRAM) [11]. While this approach performed well, significant changes to the model are required for other types of multistate devices to work with it.…”

Section: Introductionmentioning

confidence: 99%

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A Generalized Workflow for Creating Machine Learning-Powered Compact Models for Multi-State Devices

et al. 2022

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The predictive capability of existing physical descriptions of multi-state devices (e.g., oxide memristors, ferroelectrics, antiferroelectric, etc.) cannot be fully leveraged in circuit simulations due to practical limitations regarding the complexity of compact models. We attempt to circumvent this issue by adopting a machine-learning (ML) -based approach to develop a compact model that retains the full physical description of these devices. MLbased modeling approaches have garnered immense interest in recent years and have already been successfully utilized in making models for several novel devices. A known hurdle for ML-based compact modeling is the need for large amount of experimental data to properly train the model. We propose a method to simulate additional data by duplicating the data and adding Gaussian Noise to the duplicates. We propose a generalized framework to -(i) facilitate efficient training of ML-based device models, (ii) conduct seamless conversion to Verilog-A model, and (iii) interface with industry-standard circuit simulators (HSPICE, SPECTRE, etc.). We demonstrate the capabilities of our framework using the hafnium oxide (HfOx) memristor as a test device. As the source of the training data, we use a physical model that unifies detailed atomic-level descriptions with self-consistent evaluation of electronic transport. In addition, we test our model with experimental data for multiple memristor samples and repeated cycles of the same sample. Our ML-based framework prepares a circuit-compatible compact model to facilitate system-level simulations. With our model, we achieve a root mean squared error (RMSE) of 0.000863 and an R 2 of 0.977371 on our testing data.

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An effective oxygen vacancy restrain method for flexible perovskite solar cells with enhanced performance and bending resistance

Tang,

Lei,

et al. 2023

Applied Surface Science

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RRAM Compact Modeling Using Physics and Machine Learning Hybridization

Cited by 9 publications

References 24 publications

A Physics-Informed Recurrent Neural Network for RRAM Modeling

A Physics-Informed Recurrent Neural Network for RRAM Modeling

A Generalized Workflow for Creating Machine Learning-Powered Compact Models for Multi-State Devices

An effective oxygen vacancy restrain method for flexible perovskite solar cells with enhanced performance and bending resistance

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