Neural Network Simulation Research Articles

Artificial photoelectric synaptic devices with ferroelectric diode effect for high-performance neuromorphic computing

With the rapid advancement of artificial intelligence and machine learning, the demand for neuromorphic computing systems has intensified. High-performance artificial synaptic devices are crucial to achieving this objective. Ferroelectric diode artificial synaptic devices were prepared using aluminum-doped BaTiO3 (BTAO) thin films by a low-cost sol-gel method. These devices mimic the basic properties of biological synapses, such as long-term plasticity (LTP) and short-term plasticity (STP), under electrical stimulation. Additionally, the devices exhibit an excellent UV light response, enabling the transition from STP to LTP by adjusting the light pulse intensity, width, and number of pulses. Aluminum doping significantly enhances the ferroelectricity of BaTiO3 films, increasing the Pr from 2.31 µC/cm² to 9.08 µC/cm². By modulating the Schottky barrier through polarization, the BTAO films exhibit a switchable diode effect, which facilitates fine-tuning of the synaptic connection strength while maintaining synaptic stability. Recognition accuracies of 97.32% for the MNIST dataset and 87.48% for the Fashion-MNIST dataset were achieved by convolutional neural network simulations. These results suggest new possibilities for brain-like processing with ferroelectric diodes.

Various optimized artificial neural network simulations of advection-diffusion processes

The aim of this research is to describe an artificial neural network (ANN) based method to approximate the solutions of the natural advection-diffusion equations. Although the solutions of these equations can be obtained by various effective numerical methods, feed forward neural network (FFNN) techniques combined with different optimization techniques offer a more practicable and flexible alternative than the traditional approaches to solve those equations. However, the ability of FFNN techniques to solve partial differential equations is a questionable issue and has not yet been fully concluded in the existing literature. The reliability and accuracy of computational results can be advanced by the choice of optimization techniques. Therefore, this study aims to take an effective step towards presenting the ability to solve the advection-diffusion equations by leveraging the inherent benefits of ANN methods while avoiding some of the limitations of traditional approaches. In this technique, the solution process requires minimizing the error generated by using a differential equation whose solution is considered as a trial solution. More specifically, this study uses a FFNN and backpropagation technique, one of the variants of the ANN method, to minimize the error and the adjustment of parameters. In the solution process, the loss function (error) needs to be minimized; this is accomplished by fitting the trial function into the differential equation using appropriate optimization techniques and obtaining the network output. Therefore, in this study, the commonly used techniques in the literature, namely gradient descent (GD), particle swarm optimization (PSO) and artificial bee colony (ABC), are selected to compare the effectiveness of gradient and gradient-free optimization techniques in solving the advection-diffusion equation. The calculations with all three optimization techniques for linear and nonlinear advection-diffusion equations have been run several times to obtain the optimum accuracy of the results. The computed results are seen to be very promising and in good agreement with the effective numerical methods and the physics-informed neural network (PINN) method in the literature. It is also concluded that the PSO-based algorithm outperforms other methods in terms of accuracy.

Commentary: Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.

Commentary: Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.

Controlling Long-Term Plasticity in Neuromorphic Computing Through Modulation of Ferroelectric Polarization.

Electrolyte-gated transistors (EGTs) have significant potential for neuromorphic computing because they can control the number of ions by mimicking neurotransmitters. However, fast depolarization of the electric double layer (EDL) makes it difficult to achieve long-term plasticity (LTP). Additionally, most research utilizing organic ferroelectric materials has been focused on basic biological functions, and the impact on nonvolatile memory properties is still lacking. Herein, we present a polyvinylidene fluoride (PVDF)-based ion-gel synaptic device using PVDF and poly(vinylidene fluoride-co-hexafluoropropylene) (P(VDF-HFP)) to implement LTP through the introduction of ferroelectric materials. The PVDF-based polymer slows the escape rate of TFSI anions from the electrolyte/channel layer through residual polarization. The fabricated synaptic devices successfully demonstrate LTP by controlling ion adsorption under the influence of PVDF-based polymers. Furthermore, it implements synaptic functions including paired pulse facilitation (PPF), high-pass filtering, and neurotransmitter control. To validate the potential of neuromorphic computing, we successfully achieved high recognition rates for artificial/convolutional neural network (A/CNN) simulations via sequential adsorption and desorption under ferroelectric polarization with long-term potentiation/depression (LTP/D). This study provides a rational ion adsorption strategy utilizing the ferroelectric polarization caused by the introduction of a PVDF-based polymer in the dielectric layer.

Analog‐Digital Hybridity of Resistive Switching in Ion‐Irradiated BiFeO3 Memristor for Synergistic Neuromorphic Functionality and Artificial Learning

AbstractMemristors‐based neuromorphic devices represent emerging computing architectures to perform complex tasks by outpacing the traditional Von‐Neumann architectures in terms of speed, and energy efficiency. In this work, the resistive switching (RS) behavior of sol‐gel grown and ion‐irradiated BFO films is investigated under electrical stimulus. The Ag/BFO/FTO memristors emulate a combination of digital and analog RS behavior within a single device. The possible mechanism of analog digital hybridity is addressed by considering the formation of the conducting filament by oxygen vacancies, Ag+ ions and Schottky barrier height modulation. The ion‐irradiated BFO samples are analyzed using the Raman, XRD, and XPS studies. To uphold bioinspired synaptic actions, crucial synaptic functionalities like pair‐pulse facilitation and long‐term potentiation/depression are effectively achieved. More intricate synaptic behaviors are also demonstrated such as spike‐time‐dependent plasticity and Pavlovian classical conditioning, which represent the prominent attributes of both learning and forgetting behavior. Additionally, high pattern recognition accuracy (96.1%) is achieved in an artificial neural network simulation by using the synaptic weights of the memristors. This synergistic effect of digital and analog RS in ion‐irradiated BFO can be beneficial for the emulation of complex learning behavior as well as its incorporation into low‐power neuromorphic computing.

Hydrogen and vacancy concentrations in [formula omitted]-iron under high hydrogen gas pressure and external stress: A first-principles neural network simulation study

The metal-hydrogen-vacancy interaction in α-iron is crucial for understanding hydrogen embrittlement behavior and developing reliable materials for green gaseous hydrogen applications; however, it remains underexplored, particularly in contexts involving external stress and high hydrogen gas pressure. In this study, we performed quantitative analyses of metal-vacancy–hydrogen interactions in α-iron, measured by hydrogen solubility and the thermodynamics of vacancy–hydrogen complexes, under such challenging conditions using a reliable first-principles neural network potential. High hydrogen gas pressures reaching up to 2 GPa were investigated. As the atomic concentration surpasses approximately 0.01, hydrogen solubility is dominated by hydrogen-induced lattice distortion (primarily volumetric expansion) and the interactions between hydrogen atoms within the metal matrix, resulting in significant deviations from Sieverts’ law. Our results reveal a significant effect of shear stress on hydrogen solubility, deviating from previously used equations. Moreover, the influence of external stress on the thermodynamics of the vacancy–hydrogen complex, particularly vacancy formation free energy, is uniquely characterized by hydrogen solubility, regardless of the external stress magnitude. It thereby offers a rapid method to estimate the vacancy properties under external stress based on readily accessible external-stress-free data.

Berezinskii–Kosterlitz–Thouless Transition of the Two-Dimensional XY Model on the Honeycomb Lattice

Abstract The Berezinskii–Kosterlitz–Thouless (BKT) transition of the two-dimensional $XY$ model on the honeycomb lattice is investigated using both the techniques of Neural network (NN) and Monte Carlo simulations. It is demonstrated in the literature that, with certain plausible assumptions, the associated critical temperature $T_{\text{BKT,H}}$ is found to be ${1}/{\sqrt{2}}$ exactly. Surprisingly, the value of $T_{\text{BKT,H}}$ obtained from our NN calculations is 0.572(3), which deviates significantly from ${1}/{\sqrt{2}}$. In addition, based on the helicity modulus, the $T_{\text{BKT,H}}$ determined is 0.576(4), agreeing well with that resulting from the NN estimation. It will be interesting to carry out a more detailed analytic calculation to obtain a theoretical value consistent with the numerical result reached here.

Defect-engineered monolayer MoS2 with enhanced memristive and synaptic functionality for neuromorphic computing

Two-dimensional transition metal dichalcogenides (TMDs)-based memristors are promising candidates for realizing artificial synapses in next-generation computing. However, practical implementation faces several challenges, such as high non-linearity and asymmetry in synaptic weight updates, limited dynamic range, and cycle-to-cycle variability. Here, utilizing optimal-power argon plasma treatment, we significantly enhance the performance matrix of memristors fabricated from monolayer MoS2. Our approach not only improves linearity and symmetry in synaptic weight updates but also increases the number of available synaptic weight updates and enhances Spike-Time Dependent Plasticity. Notably, it broadens the switching ratio by two orders, minimizes cycle-to-cycle variability, reduces non-linear factors, and achieves an energy consumption of ~30 fJ per synaptic event. Implementation of these enhancements is demonstrated through Artificial Neural Network simulations, yielding a learning accuracy of ~97% on the MNIST hand-written digits dataset. Our findings underscore the significance of defect engineering as a powerful tool in advancing the synaptic functionality of memristors.

Programming gel automata shapes using DNA instructions

The ability to transform matter between numerous physical states or shapes without wires or external devices is a major challenge for robotics and materials design. Organisms can transform their shapes using biomolecules carrying specific information and localize at sites where transitions occur. Here, we introduce gel automata, which likewise can transform between a large number of prescribed shapes in response to a combinatorial library of biomolecular instructions. Gel automata are centimeter-scale materials consisting of multiple micro-segments. A library of DNA activator sequences can each reversibly grow or shrink different micro-segments by polymerizing or depolymerizing within them. We develop DNA activator designs that maximize the extent of growth and shrinking, and a photolithography process for precisely fabricating gel automata with elaborate segmentation patterns. Guided by simulations of shape change and neural networks that evaluate gel automata designs, we create gel automata that reversibly transform between multiple, wholly distinct shapes: four different letters and every even or every odd numeral. The sequential and repeated metamorphosis of gel automata demonstrates how soft materials and robots can be digitally programmed and reprogrammed with information-bearing chemical signals.

Emulating Low-Power Synaptic Plasticity in a Solution-Processed Oxide-Based Long Retention Memory Transistor with High Learning Accuracy.

The exploration of synaptic plasticity in metal-oxide-based ferroelectric thin-film transistors has been limited. As a perovskite ferroelectric material, LiNbO3 is widely studied; but its potential use as a neuromorphic device, like synaptic transistors, has not been realized. In this study, a solution-processed ferroelectric thin-film transistor (FeTFT) with an alternating layer of LiNbO3 and Li5AlO4 as a gate dielectric has been fabricated. This configuration reduces the depolarization field by leveraging the large ionic polarization of Li+ ions in the Li5AlO4 layer, while the wide bandgap helps mitigate the leakage current. FeTFT exhibits impressive transistor performance, including a saturation mobility of 0.478 cm2V-1 s-1, an on/off ratio of 3.08 × 103, and a low trap-state density of 1.3 × 1013 cm-2. Moreover, the device demonstrates good memory retention, retaining information for nearly 1 day. It successfully emulates synaptic plasticity, specifically short-term plasticity and long-term plasticity. Besides, a 94% training accuracy has been achieved through artificial neural network simulation. Notably, the FeTFT consumes minimal power, with energy consumption of approximately 3.09 nJ per synaptic event, which is remarkably low compared to other reported solution-processed FeTFT devices.

Grey-Taguchi and ANN optimization in CI Engines using acetylene & biodiesel blends for Low Emissions and Better Performance

This study aimed to reduce smoke and NOx emissions in a diesel engine fuelled with a 20% blend of Calophyllum inophyllum and Prosopis juliflora biodiesel (B20) with neat diesel, supplemented with acetylene at flow rates of 1, 2, and 3 liters per minute (lpm) in dual-fuel mode. Using the Grey-Taguchi method and an L9 (3^3) orthogonal array, the effects of compression ratio, fuel type, and acetylene flow rate were examined. Regression models were developed to predict brake thermal efficiency, smoke, and NOx emissions based on these controllable factors. The study found that the optimal individual values for NOx, brake thermal efficiency, and smoke were 2353 ppm, 31.52%, and 48.7 ppm, respectively. The best-combined results were achieved with a compression ratio of 17.5 and an acetylene flow rate of 3 lpm using the CI20 blend. The findings demonstrated significant improvements in output response factors when the optimal combination was applied, as validated by experimental and artificial neural network (ANN) simulations. The Grey-Taguchi approach proved effective in reducing emissions while enhancing engine performance.

Reconfigurable dielectric engineered WSe2/HZO mem-transistor

Abstract Hybrid systems coupling 2D semiconductors with functional ferroelectrics are attracting increasing attention due to their excellent electronic/optoelectronic properties and new functionalities through the multiple heterointerface interaction. In our device architecture, interfacial states are introduced on the ferroelectric Hf0.5Zr0.5O2 thin film as gate dielectric layer for charge trapping effect. Utilizing the collaborative effects of charge trapping and ferroelectric polarization behavior, a multifunctional 2D WSe2/HZO memtransistor is demonstrated with ultra-low off-state (dark) current of 10-13 A, high on/off ratio of 106 and linear conductance update. This device exhibits reliable memory properties and tunable synaptic functions including short-term plasticity (STP)/ long-term plasticity (LTP), paired pulse facilitation (PPF), spike-timing dependent plasticity (STDP), synaptic potentiation/depression, and filtering in a single device. Extensive endurance tests ensure robust stability (1000 switching cycles, 2000 s holding time) and the synaptic weight update in the device exhibits excellent linearity. Based on the experimental data, our devices eventually achieve an accuracy of 94.8% in artificial neural network simulations. These results highlight a new approach for constructing hybrid systems coupling 2D semiconductors with functional ferroelectrics in a single device for tuning synaptic weight, optimizing circuit design, and building artificial neuromorphic computing systems.

Neuromorphic learning and recognition in WO3−x thin film-based forming-free flexible electronic synapses

In pursuing advanced neuromorphic applications, this study introduces the successful engineering of a flexible electronic synapse based on WO3-x, structured as W/WO3-x/Pt/Muscovite-Mica. This artificial synapse is designed to emulate crucial learning behaviors fundamental to in-memory computing. We systematically explore synaptic plasticity dynamics by implementing pulse measurements capturing potentiation and depression traits akin to biological synapses under flat and different bending conditions, thereby highlighting its potential suitability for flexible electronic applications. The findings demonstrate that the memristor accurately replicates essential properties of biological synapses, including short-term plasticity (STP), long-term plasticity (LTP), and the intriguing transition from STP to LTP. Furthermore, other variables are investigated, such as paired-pulse facilitation, spike rate-dependent plasticity, spike time-dependent plasticity, pulse duration-dependent plasticity, and pulse amplitude-dependent plasticity. Utilizing data from flat and differently bent synapses, neural network simulations for pattern recognition tasks using the Modified National Institute of Standards and Technology dataset reveal a high recognition accuracy of ∼95% with a fast learning speed that requires only 15 epochs to reach saturation.

CMOS-Compatible Single-Gated Memtransistor Having 3-D Bulk Channel Embedded with Quantum Well Memory Nodes for Fast Training of Deep Neural Network

Recently, three-terminal synaptic devices having gate, source, and drain electrodes have been actively researched to compensate for the poor reliability of two-terminal synaptic devices such as resistive random access memory (ReRAM) and phase change random access memory (PCRAM)1. Two-terminal devices inherently suffer from read disturbance problems, where the stored resistance state changes gradually during the read process of the artificial neural network2. In addition, two-terminal devices essentially require selection devices for performing the write (program or erase) and read process, which is limited to commercialization because matching of the electrical characteristics between the selection device and the synaptic device was extremely difficult3. Moreover, the training speed of artificial neural networks would degrade due to the constant on/off ratio of conductance of two-terminal synaptic devices.To solve this problem, the memtransistors that can change the conductance range of the channel with gate bias have been reported. However, most of the reported memtransistors employed 2-D materials such as MoS2, WSe2, and SnS2 as a channel layer, which is a limitation for uniform large-area thin film growth4–6. In other words, the memtransistors with a 2-D channel layer do not have CMOS compatibility.In this study, for the first time, we designed a CMOS-compatible single-gated memtransistor having an indium-gallium-zinc-oxide (IGZO) channel embedded with quantum well memory nodes via Au nanoparticles. The quantum well memory nodes were obtained by precisely designing the diameter of Au nanoparticles in the IGZO 3-D bulk channel and SiO2 gate oxide interfaces. In addition, the quantum well memory nodes using Au nanoparticles were investigated by an energy band diagram obtained from ultraviolet photoelectron spectroscopy (UPS) analysis of Au, IGZO, and Al source/drain electrodes. Thereby, the gate-tunable pinched hysteresis loops in the output curve of the designed memtransistor were achieved ranging from -30 to 30 V, which present channel conductance tuning by controlling the amplitude of gate bias. In addition, the gate-tunable synaptic plasticities (i.e., long-term potentiation (LTP) and long-term depression (LTD)) were obtained by changing the amplitude of gate bias from 10.0 to 13.5 V at an incremental of 0.5 V, exhibiting exponential growth of maximum channel conductance depending on the amplitude of gate bias. Finally, we demonstrated the fast training of the hardware-based deep neural network simulation using the gate-tunable synaptic plasticity of the designed memtransistor. The mechanism of single-gated memtransistor having quantum well memory nodes via Au nanoparticles and its application will be presented in detail. Acknowledgement This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2023-00260527) and Institute of Information & communications Technology Planning & Evaluation (IITP) under the artificial intelligence semiconductor support program to nurture the best talents (IITP-(2023)-RS-2023-00253914) grant funded by the Korea government(MSIT)" References Fuller, E. J. et al. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science (80-. ). 364, 570–574 (2019).Yoon, J., Chang, M. & Khwa, W. Compute-in-Memory RRAM Macro With Write Verification and Multi-Bit Encoding. Jssc 57, 1–13 (2022).Kim, H. J. et al. Super-Linear-Threshold-Switching Selector with Multiple Jar-Shaped Cu-Filaments in the Amorphous Ge3Se7 Resistive Switching Layer in a Cross-Point Synaptic Memristor Array. Adv. Mater. 2203643, (2022).Lee, H. S. et al. Dual-Gated MoS2 Memtransistor Crossbar Array. Adv. Funct. Mater. 30, 1–12 (2020).Ding, G. et al. Reconfigurable 2D WSe2-Based Memtransistor for Mimicking Homosynaptic and Heterosynaptic Plasticity. Small 17, 1–13 (2021).Rehman, S., Khan, M. F., Kim, H. D. & Kim, S. Analog–digital hybrid computing with SnS2 memtransistor for low-powered sensor fusion. Nat. Commun. 13, 1–8 (2022). Figure 1

A neural basis for learning sequential memory in brain loop structures.

Behaviors often involve a sequence of events, and learning and reproducing it is essential for sequential memory. Brain loop structures refer to loop-shaped inter-regional connection structures in the brain such as cortico-basal ganglia-thalamic and cortico-cerebellar loops. They are thought to play a crucial role in supporting sequential memory, but it is unclear what properties of the loop structure are important and why. In this study, we investigated conditions necessary for the learning of sequential memory in brain loop structures via computational modeling. We assumed that sequential memory emerges due to delayed information transmission in loop structures and presented a basic neural activity model and validated our theoretical considerations with spiking neural network simulations. Based on this model, we described the factors for the learning of sequential memory: first, the information transmission delay should decrease as the size of the loop structure increases; and second, the likelihood of the learning of sequential memory increases as the size of the loop structure increases and soon saturates. Combining these factors, we showed that moderate-sized brain loop structures are advantageous for the learning of sequential memory due to the physiological restrictions of information transmission delay. Our results will help us better understand the relationship between sequential memory and brain loop structures.

Editor's Note for article entitled “Prediction of the thermal behaviour of Li-ion battery packs in various configurations. Combination of pattern-based artificial neural network (PBANN) and numerical simulation”

Editor's Note for article entitled “Prediction of the thermal behaviour of Li-ion battery packs in various configurations. Combination of pattern-based artificial neural network (PBANN) and numerical simulation”

Integrating Experimental and Numerical Data for Improved Steam Reforming Simulation with Deep Learning

Abstract In this paper, a data-driven methane steam reforming simulation is developed and used to predict the post-reaction mixture composition. Until today, methane steam reforming remains a predominant hydrogen production method, yet modeling its complex reactions remains a significant challenge due to the intricate interplay of process variables. Here, we show an artificial neural network simulator that can effectively model these reactions, offering precise predictions based on parameters like temperature, inlet gas composition, methane flow, and nickel catalyst mass. Our approach to data curation integrates experimental, interpolated, and theoretically calculated values and refining the model by assessing the relative importance of each data category. Various neural network structures were tested before ultimately identifying an optimal architecture with a 5-6-8-6-4 network structure. The network underwent 6000 epochs of training, leading to a model that demonstrates excellent agreement with experimental observations, as evidenced by the mean squared error of 0.000217 and the Pearson correlation coefficient of 0.965. Moreover, all process trajectories predicted by the network are characterized by a smooth course and are within a physical range of values. Therefore, this work overcomes a common challenge in chemical process simulation using neural networks and also sets a possible direction for future research in this field.

Evidence of ferroelectric features in low-density supercooled water from ab initio deep neural-network simulations

Over the last decade, an increasing body of evidence has emerged, supporting the existence of a metastable liquid-liquid critical point in supercooled water whereby two distinct liquid phases of different densities coexist. Analyzing long molecular dynamics simulations performed using deep neural-network force fields trained to accurate quantum mechanical data, we demonstrate that the low-density liquid phase displays a strong propensity toward spontaneous polarization, as witnessed by large and long-lived collective dipole fluctuations. Our findings suggest that the dynamical stability of the low-density phase, and hence the transition from high-density to low-density liquid, is triggered by a collective process involving an accumulation of rotational angular jumps, which could ignite large dipole fluctuations. This dynamical transition involves subtle changes in the electronic polarizability of water molecules which affects their rotational mobility within the two phases. These findings hold the potential for catalyzing activity in the search for dielectric-based probes of the putative second critical point.

Modulating Neuromorphic Behavior of Organic Synaptic Electrolyte-Gated Transistors Through Microstructure Engineering and Potential Applications.

Organic synaptic transistors are a promising technology for advanced electronic devices with simultaneous computing and memory functions and for the application of artificial neural networks. In this study, the neuromorphic electrical characteristics of organic synaptic electrolyte-gated transistors are correlated with the microstructural and interfacial properties of the active layers. This is accomplished by utilizing a semiconducting/insulating polyblend-based pseudobilayer with embedded source and drain electrodes, referred to as PB-ESD architecture. Three variations of poly(3-hexylthiophene) (P3HT)/poly(methyl methacrylate) (PMMA) PB-ESD-based organic synaptic transistors are fabricated, each exhibiting distinct microstructures and electrical characteristics, thus serving excellent samples for exploring the critical factors influencing neuro-electrical properties. Poor microstructures of P3HT within the active layer and a flat active layer/ion-gel interface correspond to typical neuromorphic behaviors such as potentiated excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and short-term potentiation (STP). Conversely, superior microstructures of P3HT and a rough active layer/ion-gel interface correspond to significantly higher channel conductance and enhanced EPSC and PPF characteristics as well as long-term potentiation behavior. Such devices were further applied to the simulation of neural networks, which produced a good recognition accuracy. However, excessive PMMA penetration into the P3HT conducting channel leads to features of a depressed EPSC and paired-pulse depression, which are uncommon in organic synaptic transistors. The inclusion of a second gate electrode enables the as-prepared organic synaptic transistors to function as two-input synaptic logic gates, performing various logical operations and effectively mimicking neural modulation functions. Microstructure and interface engineering is an effective method to modulate the neuromorphic behavior of organic synaptic transistors and advance the development of bionic artificial neural networks.

Maximizing the Synaptic Efficiency of Ferroelectric Tunnel Junction Devices Using a Switching Mechanism Hidden in an Identical Pulse Programming Learning Scheme

Memristors play a pivotal role in advanced computing, with memristor‐based crossbar arrays showing promise for various artificial neural networks. Among these, HfO2‐based ferroelectric tunnel junctions (FTJs) stand out as ideal synaptic devices for neuromorphic computing. Their compatibility with the complementary metal oxide semiconductor process and intrinsic energy efficiency make them particularly appealing. While an increasing number of studies adopt identical pulse programming (IPP) with short width to update the conductance of HfO2‐based FTJs synaptic devices, conventional ferroelectric switching models fall short in describing updates the conductance with the IPP scheme. Consequently, studies achieving conductance updates via IPP lack an underlying mechanism explanation, potentially limiting the application of HfO2‐based FTJs as synaptic devices. This study explores the potential of ferroelectric Zr‐doped HfO2 (HZO) FTJs to undergo learning through the IPP scheme. Synaptic characteristics, including the number of conductance states, symmetry, linearity, write energy, and latency by modulating IPP scheme conditions are optimized. Finally, the applicability of HZO FTJ as a synaptic device by assessing learning accuracy in pattern recognition through artificial neural network simulation based on the optimized synaptic characteristics is evaluated.