KR-20260065498-A - Data-based Automatic Calibration Method and System for Electrical Models of ReRAM

KR20260065498AKR 20260065498 AKR20260065498 AKR 20260065498AKR-20260065498-A

Abstract

A data-based automatic calibration method and system for an electrical model for a resistance change memory are presented. The data-based automatic calibration method for an electrical model for a resistance change memory proposed in the present invention includes a step of extracting bounds of characteristic information under a plurality of conditions by utilizing a library through a first algorithm execution unit, and a step of tuning bounds of characteristic information under a plurality of conditions through iterative learning based on the bounds of characteristic information extracted by the first algorithm execution unit through a second algorithm execution unit.

Inventors

정성엽
이태헌
이오준
조민선
정진우

Assignees

고려대학교 세종산학협력단

Dates

Publication Date: 20260508
Application Date: 20250917
Priority Date: 20241101

Claims (10)

A step of extracting bounds of characteristic information under multiple conditions by utilizing a library through a first algorithm execution unit; and A step of tuning the bounds of feature information under multiple conditions through iterative learning, based on the bounds of feature information extracted from the first algorithm execution unit through the second algorithm execution unit. A data-based automatic calibration method for a compact model of a resistance change memory including
In paragraph 1, The step of extracting bounds of characteristic information under multiple conditions by utilizing a library through the first algorithm execution unit above is: The global optimization algorithm and the local optimization algorithm for the model variables are executed sequentially, and At each step of the global optimization algorithm and the local optimization algorithm, the global optimization algorithm and the local optimization algorithm are repeated until a predetermined accuracy is reached or a predetermined current level is reached. Data-based automatic calibration method for an electrical model for resistance change memory.
In paragraph 2, The step of extracting bounds of characteristic information under multiple conditions by utilizing a library through the first algorithm execution unit above is: Based on constraints on model variables Applying a technique to expand the scope of the search by adding an algorithm to prevent convergence to local intervals in both the global optimization algorithm and the local optimization algorithm. Data-based automatic calibration method for an electrical model for resistance change memory.
In paragraph 1, The step of tuning the bounds of feature information under multiple conditions through iterative learning based on the bounds of feature information extracted from the first algorithm execution unit through the second algorithm execution unit, Based on the bounds of feature information extracted in the first algorithm execution unit, an initial bound is set, and the bounds of feature information under multiple conditions are tuned through iterative learning based on correlation values for model variables. Data-based automatic calibration method for an electrical model for resistance change memory.
In paragraph 4, The step of tuning the bounds of feature information under multiple conditions through iterative learning based on the bounds of feature information extracted from the first algorithm execution unit through the second algorithm execution unit, Extracting a bound that satisfies a predetermined goal by optimizing the bounds of feature information under multiple conditions using the results of tuning the bounds of feature information under multiple conditions through iterative learning. Data-based automatic calibration method for an electrical model for resistance change memory.
A first algorithm execution unit that extracts bounds of feature information under multiple conditions using a library; and A second algorithm execution unit that tunes the bounds of feature information under multiple conditions through iterative learning, based on the bounds of feature information extracted by the first algorithm execution unit. A data-based automatic calibration system for an electrical model of a resistance change memory including
In paragraph 6, The above-mentioned first algorithm execution unit is, The global optimization algorithm and the local optimization algorithm for the model variables are executed sequentially, and At each step of the global optimization algorithm and the local optimization algorithm, the global optimization algorithm and the local optimization algorithm are repeated until a predetermined accuracy is reached or a predetermined current level is reached. Data-based automatic calibration system for electrical models of resistance change memory.
In Paragraph 7, The above-mentioned first algorithm execution unit is, Based on constraints on model variables, Applying a technique to expand the scope of the search by adding an algorithm to prevent convergence to local intervals in both the global optimization algorithm and the local optimization algorithm. Data-based automatic calibration system for electrical models of resistance change memory.
In paragraph 6, The above second algorithm execution unit is, Based on the bounds of feature information extracted in the first algorithm execution unit, an initial bound is set, and the bounds of feature information under multiple conditions are tuned through iterative learning based on correlation values for model variables. Data-based automatic calibration system for electrical models of resistance change memory.
In Paragraph 9, The above second algorithm execution unit is, Extracting a bound that satisfies a predetermined goal by optimizing the bounds of feature information under multiple conditions using the results of tuning the bounds of feature information under multiple conditions through iterative learning. Data-based automatic calibration system for electrical models of resistance change memory.

Description

Data-based Automatic Calibration Method and System for Electrical Models of ReRAM The present invention relates to a data-based automatic calibration method and system for an electrical model of a Resistance Random Access Memory (ReRAM). Resistive switching memory devices, which utilize the characteristic of a resistive switching material whose resistance value changes depending on voltage conditions, can operate with only a simple structure of electrode layer-resistive switching material layer-electrode layer and have advantages in terms of fast switching speed and low-voltage operation. Unlike other next-generation non-volatile memory devices, these resistive switching memory devices can increase integration density through a simple process, and the manufacturing cost is low. Resistive change memory devices are generally implemented in the form of a crossbar array, and a selector is placed in each memory cell to operate the device so that current can flow only to the cell to be operated. A resistance switching memory device comprises a material layer having two states, wherein a conductive region with a low resistance value (Low Resistance Stage; LRS) is formed depending on voltage conditions, and a state with a high resistance value (High Resistance Stage; HRS) is formed when the conductive region is broken, and it has a switching function through the distinction between the two states. Based on this, since single-layer resistive switching memory devices had a problem of insufficient switching capability because they could not have sufficiently large resistance in the HRS state, a double-layer material was applied to the resistive switching memory device to secure switching capability while having sufficiently large resistance in the HRS state. However, unlike single-layer resistive memory devices, it is unclear at which point in the dual layer filament formation and extinction caused by oxygen vacancies occur in dual-layer resistive memory devices. Consequently, dual-layer resistive memory devices require inspection when high-density integrated circuits are fabricated and operated, and it is difficult to analyze the cause of malfunctions or operational failures. Resistive switching memory is a next-generation AI semiconductor that performs computations within memory, going beyond the von Neumann architecture of existing first-generation AI. It is expected to increase power efficiency and improve computational performance, and is becoming increasingly important as a technology that will enable the application of ICT technologies. Meanwhile, in resistance change memory devices composed of a metal-insulator-metal structure, various switching characteristics occur depending on the type of insulator or the interface state between the metal and the insulator; therefore, there are various models that simulate the related physical variables and device characteristics. Most existing methods for calculating model variables are limited to presenting maximum and minimum parameter ranges and manually searching for variables using restricted algorithms. In particular, this approach has limitations in terms of computational efficiency and the reflection of generalized characteristics. Furthermore, commercially available circuit simulation-based optimization techniques are limited to modeling transistors or diodes and are not suitable for devices requiring time-domain analysis techniques, such as Resistive Random Access Memory (ReRAM). Accordingly, the present invention proposes a method that enables the simulation of non-linear I-V that varies depending on the application of various input waveforms and can improve computational efficiency through a technique for calculating the range of model variables. Figure 1 is a diagram showing the structure of a SiOx-based resistance change memory. Figure 2 is a diagram illustrating the state of a resistance change memory. FIG. 3 is a flowchart illustrating a data-based automatic calibration method for an electrical model for a resistance change memory according to an embodiment of the present invention. FIG. 4 is a diagram showing the configuration of a data-based automatic calibration system for an electrical model for a resistance change memory according to an embodiment of the present invention. FIG. 5 is a diagram showing calibration results and waveform-specific accuracy results according to an embodiment of the present invention. FIG. 6 is a diagram showing the TCAD results of calibration applied using the first algorithm and the second algorithm according to one embodiment of the present invention. FIG. 7 is a diagram showing the measurement results of calibration applied with a first algorithm and a second algorithm according to an embodiment of the present invention. FIG. 8 is a diagram showing the results of accuracy improvement as iteration proceeds according to one embodiment of the present invention. Hereinafter, embodiments of the present invention will be describ