Abstract
With the immense increase in the processing data during the scaling down of semiconductor devices by Moore's Law, it is in urgent need to use data analytics to meet the state of the art performance in both manufacturing and device compact modeling. In particular, managing the fabrication cost and promptly providing compact device models, especially for new or emerging devices, is challenging. To ease out these issues, we propose a unified, general-purpose, process-aware machine learning (ML) based compact model (CM) for resistive random-access memory (RRAM), and the same methodology can be used for any memory devices with hysteresis. A long short-term memory (LSTM) ML model is used to fit the RRAM current-voltage (I-V) characteristics. The memorizing capability of LSTM ensures one model can fit both RRAM low resistance state (LRS) and high resistance state (HRS). The fitted dataset is based on the fabricated RRAM samples using TaN/HfO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /Pt/Ti/SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /Si structure. The resultant fitting error is 0.0096 in sinusoidal wave input voltage and 0.0148 in random walk voltage sequences. In the process-aware demonstration, we use post-oxide annealing dataset from 300°C to 500°C. The root mean squared error (RMSE) in the process-aware RRAM compact model is 0.0028. Thus, the LSTM-based CM has the potential to compete with the conventional compact device models in terms of shorter developing time, better fitting capability in emerging devices and a large number of devices, easily incorporated process-aware models, and one unified model accounting for LRS and HRS ensuring differentiability. We propose that the LSTM based memory CM can be useful in intelligent manufacturing, process tuning, and simulation program with integrated circuit emphasis (SPICE) modeling in circuit simulation.
Highlights
A memory unit is an integral part of any computing system and in 1967, Kahng and Sze revolutionized the semiconductor memory industry by introducing the concept of a floating gate in a non-volatile type memory device [1]
root mean squared error (RMSE) is defined as the square root of the mean squared error (MSE), where MSE is expressed as the summation of the square of the difference between the predicted values and the true values, divided by the total number of data points
The recurrent nature and the state-dependent operation of long-short term memory (LSTM) properly take into account the resistive random-access memory (RRAM) I-V with hysteresis
Summary
A memory unit is an integral part of any computing system and in 1967, Kahng and Sze revolutionized the semiconductor memory industry by introducing the concept of a floating gate in a non-volatile type memory device [1]. This concept brought a revolution in the field of the semiconductor memory industry. With excellent read/write operation, better endurance, and retention [4], [5], this device has the potential to meet the expectations of memory industries.
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