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  1. (Chungnam National University, Republic of Korea)
  2. (Department of Green Semiconductor System, Korea Polytechnics Daegu Campus 15, Gukchaebosang-ro 43-gil, Seo-gu, Daegu, Republic of Korea)
  3. (School of Electronic & Electrical Engineering and Research Center for Hyper-Connected Convergence Technology, Hankyong National University, 327 Jungang-ro, Anseong-si, Gyeonggi-do, Republic of Korea)



Memristive device, HfO2/ZnO, work function, resistive switching, synaptic device conduction mechanism, long term potentiation, long-term depression

I. INTRODUCTION

Rapid development of artificial intelligence (AI) and data-intensive applications have breached the limitations of von Neumann architecture, where separate memory and processing units cause latency and high energy consumption [1- 4]. Neuromorphic computing, inspired by biological neural networks, integrates storage and computation in a single element to enable low-power parallel processing [5- 7].

Among emerging memories, resistive random-access memory (RRAM) is promising due to its simple metal-insulator-metal (MIM) structure, fast switching, non-volatility, and CMOS compatibility [8- 11]. RRAM can emulate synaptic plasticity through gradual conductance modulation, and multi-layer oxide stacks improve switching uniformity and analog tuneability [12- 15]. In particular, HfO2/ZnO bi-layer devices show stable switching and reliable weight modulation: HfO2 provides dielectric stability, while ZnO supports filament formation [16- 19]. Beyond the switching layers, electrode materials influence device behavior by affecting switching thresholds and conduction mechanisms [20]. Properties such as work function and Gibbs free energy of oxide formation impact filament dynamics [4, 7, 14]. A larger work function difference between TE and BE enhances the internal electric field, reducing forming and set voltages, while a low Gibbs free energy promotes oxygen ion migration and endurance [15, 17, 19]. In this work, we investigated HfO2/ZnO based devices with four TE materials: Pd, Ti, TiN, and Ti/Au, and analyzed switching voltages, conduction mechanisms, and LTP/LTD behavior. The results provide guidelines for optimizing electrode selection for reliable neuromorphic systems [1, 5, 19, 20]. Previous studies have investigated top-electrode effects on resistive switching, but they typically compare only a few metals and focus mainly on forming and SET voltage differences without explicitly linking these trends to material-dependent properties, such as trap levels or electrode-dependent filament stability [21, 22]. In contrast, this work evaluates four electrodes within the same HfO2/ZnO structure and correlates their forming, SET, and synaptic behavior with both work-function differences and the Gibbs free energy of oxide formation, offering a more comprehensive material-driven understanding of electrode effects.

II. DEVICE FABRICATION

The memristive devices were fabricated on thermally oxidized silicon substrates (Si/SiO2) according to the structure depicted in Fig. 1. The substrates were cleaned with dilute hydrofluoric acid to remove native oxide before the deposition of Ti/Pt (10/100 nm) bottom electrode. The HfO2 and ZnO insulating layers were sequentially deposited with atomic layer deposition, which provides precise control of film thickness and uniformity. Thickness of the ZnO and HfO2 layers was fixed at 15 and 3 nm, respectively. These bi-layered structures served as the active switching medium.

Fig. 1. Schematic structure of the fabricated HfO2/ZnO-based memristive device.

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TE consisting of Pd, Ti, and TiN were deposited by radio-frequency sputtering, while the Ti/Au (10 nm/100 nm) electrode was deposited using e-beam evaporation. The Ti and Au layers were sequentially stacked to form the Ti/Au bi-layer electrode. A standard lift-off process was used to define the crossbar structures of the top electrodes. The final device had a MIM structure with a cell area of 20 µm × 20 µm, which is suitable to evaluate resistive switching and synaptic characteristics.

III. RESULTS AND DISCUSSION

1. Electrical Characterization

DC electrical measurements were performed using an Agilent 4156C semiconductor parameter analyzer with a voltage sweep applied to the TE keeping the BE grounded. Unless otherwise stated, all of the measurements were conducted at room temperature. The SET and RESET compliance currents were fixed at 1 mA and 100 mA, respectively, and all devices were evaluated at the 50th cycle while repeating voltage sweeps (step: 0.05 V) until hard breakdown occurred. Table 1 summarizes the work function values of each TE material, including the effective work function of the Ti/Au (10 nm/100 nm) bi-layer electrode, and their differences with the BE. To further assess switching stability, cycle-to-cycle (C2C) and device-to-device (D2D) variations were statistically analyzed, as shown in Fig. 2.

Table 1. Work function values of BE and TE materials and their differences.

Sample BE material BE work function [eV] TE material TE work function [eV] Work function difference [eV]
#1 Pt 5.12 [21] Pd 5.60 [22] 0.48
#2 Pt 5.12 Ti 4.33 [23] 0.79
#3 Pt 5.12 TiN 4.70 [24] 0.42
#4 Pt 5.12 Ti/Au 5.10 [25] 0.02

Fig. 2. (a) C2C variation of HRS and LRS over 50 cycles for four TE devices. (b) D2D variation of SET voltage extracted from 20 devices per TE.

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Fig. 3 shows the forming and SET voltages as a function of the work function difference for all TE materials (Pd, Ti, TiN, and Ti/Au). Devices with a larger difference exhibit lower forming and SET voltages, while those with a smaller difference require higher voltages. For instance, the Ti-based device, with the largest difference, showed the lowest potential for forming (3.19 V) and SET (1.42 V), whereas the Ti/Au-based device exhibits the highest values. This behavior arises from the enhanced internal electric field of a larger work function difference, which promotes filament formation and stabilizes subsequent switching events, thereby contributing to more reliable device operation.

Fig. 3. Forming and SET voltages of devices with different TE materials (Pd, Ti, TiN, Ti/Au) and their work function differences.

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2. Conduction Mechanism Analysis

Although the TE materials differ, the insulating layers remain identical across all of the devices, suggesting no major variation in switching mechanisms. As Shown in Fig. 4, all devices exhibited Ohmic conduction in the low-voltage region and space-charge-limited conduction (SCLC) at higher voltages. This consistency shows that the work function difference affects the switching voltage rather than the conduction mechanism.

Fig. 4. (a)-(d) I-V characteristics of HfO2/ZnO devices employing Pd, Ti, TiN, and Ti/Au electrodes, showing switching behavior variations depending on the top metal. (e)-(h) Corresponding SCLC fitting results for devices with Pd, Ti, TiN, and Ti/Au.

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The Gibbs free energy of oxide formation represents the thermodynamic driving force for interfacial oxidation, where a more negative value indicates a higher tendency for spontaneous and stable oxide formation. This interfacial energetics directly influences ion transport behavior in oxide-based devices. Ti, with a low formation energy for TiO2 ($\Delta G^\circ_f \approx -888.8$ kJ/mol) [26], enables partial control of oxygen ion migration, suppressing complete filament rupture and leading to a gradual reset transition. This moderated process reduces power dissipation, alleviates stress concentration, and prevents local overheating, as illustrated in Fig. 5. Reported Gibbs free-energy data show that PdO forms near −100 kJ/mol, while gold oxides have positive values and remain unstable under common conditions [29, 30]. TiN also requires higher energies than Ti to form TiON or TiO2 [31]. These values indicate weaker interfacial oxide formation for Pd, TiN, and Au, leading to less uniform vacancy redistribution and supporting the smoother RESET and stronger retention of Ti-based devices.

Fig. 5. Schematic diagram of oxygen ion/vacancy migration in Ti TE devices during (a) SET and (b) RESET processes based on Gibbs free energy [32- 34].

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Fig. 6 shows the different reliability behaviors between Ti and Ti/Au TE devices. The Ti TE device Fig. 6(a) maintains high resistance state (HRS) values under prolonged stress, demonstrating stable retention without abrupt resistance collapse.

Fig. 6. Retention characteristics under stress voltage for devices with (a) Ti TE and (b) Ti/Au TE, showing stable operation for Ti and abrupt failure for Ti/Au.

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In contrast, the Ti/Au TE device Fig. 6(b) exhibits sudden failure under elevated stress voltages, as indicated by the sharp resistance drop labeled as “failure.” Specifically, failures were observed after approximately 1400 s at Vstress = −1.2 V and after 200 s at Vstress = −1.5 V, confirming the vulnerability of Ti/Au electrodes under high-field stress. In this work, “failure” refers to the abrupt transition from the HRS to the LRS state upon reaching the compliance current during constant voltage stress, indicating uncontrolled filament overgrowth rather than gradual resistance evolution. This discrepancy underscores that the lower Gibbs free energy of Ti facilitates controlled migration of oxygen vacancies, thereby suppressing catastrophic filament rupture and enabling gradual resistance modulation, whereas electrode systems with higher Gibbs free energy, such as Ti/Au, are more prone to abrupt breakdown.

3. Synaptic Characteristics

To evaluate synaptic performance, LTP and LTD measurements were performed by applying 100 consecutive pulses in sequence, as shown in Fig. 7. Both devices were tested using identical pulse schemes consisting of 100 potentiation (LTP) and 100 depression (LTD) pulses with a fixed pulse width of 800 µs. The pulse amplitudes were set to 2.0 V and 2.2 V for the two devices, respectively, and the read voltage was fixed at 0.2 V. All devices exhibited analog conductance modulation, demonstrating stable synaptic switching behavior and acceptable nonlinearity, which is essential for neuromorphic system implementation.

The nonlinearity (NL) was calculated using Eq. (1), where G denotes the normalized conductance and GLinear is the ideal linear conductance, as defined in [35].

(1)
$Nonlinearity = average \left( \left| \frac{G - G_{Linear}}{G_{Linear}} \right| \right)$

The nonlinearity was calculated using the same normalization range and averaging procedure for all devices to ensure a fair comparison.

In particular, the Ti-based device (Fig. 7(a)) exhibited the most gradual and symmetric conductance changes during potentiation and depression. This behavior can be attributed to the controlled migration of oxygen vacancies enabled by the low Gibbs free energy of Ti TE and the moderate work function, which together suppress abrupt filament dissolution and allow fine-tuned weight updates. Consistently, the extracted NL value of the Ti device was 0.51, close to the ideal case, indicating its suitability for stable synaptic weight updates.

Fig. 7. LTP/LTD characteristics under spike stimuli for (a) Ti TE device, (b) Ti/Au TE device, and (c) normalized conductance with extracted NL values.

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In contrast, the Ti/Au-based device in Fig. 7(b) showed less gradual modulation with higher NL values (0.90 for potentiation and 0.56 for depression), reflecting the weaker influence of its higher Gibbs free energy. Such quantitative differences highlight that Ti electrodes enable more linear conductance modulation compared to Ti/Au, which is highly desirable for reliable learning behavior in neuromorphic applications.

These results, summarized in Fig. 7, confirm that the choice of top electrode material significantly affects both the resistive switching characteristics and the synaptic plasticity of HfO2/ZnO-based memristive devices.

IV. CONCLUSIONS

In this study, we investigated the effect of TE materials on the switching behavior and synaptic performance of HfO2/ZnO-based memristive devices. All of the devices share same insulator structure, leading to a consistent switching mechanism–Ohmic conduction in the low-voltage regime and SCLC in the high-voltage regime.

Nevertheless, the TE work function and Gibbs free energy significantly influenced device performance. Devices with a larger work function difference exhibited lower forming and SET voltages. In particular, the Ti TE device, with its low Gibbs free energy, demonstrated a gradual reset process by controlling oxygen ion migration and preventing abrupt filament rupture. This thermal moderation also contributed to enhanced retention performance, with no failure observed under increased stress conditions.

Furthermore, the Ti TE device exhibited the most desirable synaptic behavior, showing gradual and symmetric conductance modulation under LTP/LTD pulse sequences. These results confirm that the selection of TE material is critical not only for resistive switching characteristics but also for achieving reliable and analog synaptic functionality in neuromorphic systems.

ACKNOWLEDGMENTS

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00249430, NRF 2022R1G1A10089 54A), and National Nano Fab Center (NNFC) grant funded by the Korea government (MSIT) (RS-2024-00441236). This work was also supported by the Korea Foundation for Women In Science, Engineering and Technology (WISET) grant, funded by the Ministry of Science and ICT (MSIT) under the Team Research Program for female engineering students. This work was supported by BK21 FOUR Program by Chungnam National University Research Grant, 2025. This research was supported by the Regional Innovation System & Education (RISE) program through the Daejeon RISE Center, funded by the Ministry of Education (MOE) and the Daejeon Metropolitan City, Republic of Korea.(No. 2025-RISE-06-012).

REFERENCES

1 
Upadhyay N. K. , Yin X. , Zhang W. , Wong H.-S. P. , 2019, Emerging memory devices for neuromorphic computing, Advanced Materials Technologies, Vol. 4, No. 4DOI
2 
Indiveri G. , Liu S.-C. , 2015, Memory and information processing in neuromorphic systems, Proceedings of the Institute of Electrical and Electronics Engineers, Vol. 103, No. 8, pp. 1379-1397DOI
3 
Zidan M. A. , Strachan J. P. , Lu W. D. , 2018, The future of electronics based on memristive systems, Nature Electronics, Vol. 1, pp. 22-29DOI
4 
Waser R. , Aono M. , 2007, Nanoionics-based resistive switching memories, Nature Materials, Vol. 6, No. 11, pp. 833-840DOI
5 
Ielmini D. , 2016, Resistive switching memories based on metal oxides: Mechanisms, reliability and scaling, Semiconductor Science and Technology, Vol. 31, No. 6DOI
6 
Sawa A. , 2008, Resistive switching in transition metal oxides, Materials Today, Vol. 11, No. 6, pp. 28-36DOI
7 
Wong H.-S. P. , Lee H.-Y. , Yu S. , Chen Y.-S. , Wu Y. , Chen P.-S. , Lee B. , Chen F.-T. , Tsai M.-J. , 2012, Metal-oxide RRAM, Proceedings of the Institute of Electrical and Electronics Engineers, Vol. 100, No. 6, pp. 1951-1970DOI
8 
Yang J. J. , Pickett M. D. , Li X. , Ohlberg D. A. A. , Stewart D. R. , Williams R. S. , 2008, Memristive switching mechanism for metal/oxide/metal nanodevices, Nature Nanotechnology, Vol. 3, pp. 429-433DOI
9 
Gao B. , Kang J. F. , Chen Y. S. , Zhang F. F. , Chen B. , Huang P. , Liu L. F. , Liu X. Y. , Wang Y. Y. , Tran X. A. , Wang Z. R. , Yu H. Y. , Chin A. , 2011, Oxide-based RRAM: Unified microscopic principle for both unipolar and bipolar switching, Proc. of the 2011 IEEE International Electron Devices MeetingDOI
10 
Ambrogio S. , Narayanan P. , Tsai H. , 2018, Equivalent-accuracy accelerated neural-network training using analogue memory, Nature, Vol. 558, pp. 60-67DOI
11 
Wang Z. , Wu H. , Burr G. W. , Hwang C. S. , Wang K. L. , Xia Q. , Yang J. J. , 2017, Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing, Nature Materials, Vol. 16, No. 1, pp. 101-108DOI
12 
Valov I. , Linn E. , Tappertzhofen S. , Schmelzer S. , van den Hurk J. , Waser R. , 2013, Nanobatteries in redox-based resistive switches require extension of memristor theory, Nature Communications, Vol. 4DOI
13 
Xia Q. , Yang J. J. , 2019, Memristive crossbar arrays for brain-inspired computing, Nature Materials, Vol. 18, pp. 309-323DOI
14 
Lanza M. , 2014, A review on resistive switching in high-k dielectrics: a nanoscale point of view using conductive atomic force microscope, Materials, Vol. 7, No. 3, pp. 2155-2182DOI
15 
Pan F. , Gao S. , Chen C. , Song C. , Zeng F. , 2014, Recent progress in resistive random access memories: materials, switching mechanisms, and performance, Materials Science and Engineering: R: Reports, Vol. 83, pp. 1-59DOI
16 
Jo S. H. , Chang T. , Ebong I. , Bhadviya B. B. , Mazumder P. , Lu W. , 2010, Nanoscale memristor device as synapse in neuromorphic systems, Nano Letters, Vol. 10, No. 4, pp. 1297-1301DOI
17 
Ielmini D. , Wong H.-S. P. , 2018, In-memory computing with resistive switching devices, Nature Electronics, Vol. 1, pp. 333-343DOI
18 
Lee M.-J. , Lee C.-B. , Lee D. , Lee S. R. , Chang M. , Hur J. H. , Kim Y.-B. , Kim C.-J. , Seo D. H. , Seo S. , Chung U.-I. , Yoo I.-K. , Kim K. , 2011, A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5-x/TaO2-x bilayer structures, Nature Materials, Vol. 10, No. 8, pp. 625-630DOI
19 
Jang J. T. , Kim H. J. , Kim Y. H. , Kim K. M. , Hwang C. S. , 2020, Digital and analog switching characteristics of InGaZnO memristor depending on top electrode material for neuromorphic system, IEEE Access, Vol. 8, pp. 192304-192311DOI
20 
Burr G. W. , Shelby R. M. , Sebastian A. , 2017, Neuromorphic computing using non-volatile memory, Advances in Physics: X, Vol. 2, No. 1, pp. 89-124DOI
21 
Hong S. M. , Kim H.-D. , An H.-M. , Kim T. G. , 2013, Effect of work function difference between top and bottom electrodes on the resistive switching properties of SiN films, IEEE Electron Device Letters, Vol. 34, No. 9, pp. 1181-1183DOI
22 
Lee T. S. , Lee N. J. , Abbas H. , Lee H. H. , Yoon T.-S. , Kang C. J. , 2020, Compliance current-controlled conducting filament formation in tantalum oxide-based RRAM devices with different top electrodes, ACS Applied Electronic Materials, Vol. 2, No. 3, pp. 1154-1161DOI
23 
Golosov D. A. , Okojie J. E. , Zavadski S. M. , Rudenkov A. S. , Melnikov S. N. , Kolos V. V. , 2018, Stability of the platinum electrode during high temperature annealing, Thin Solid Films, Vol. 661, pp. 53-59DOI
24 
Murata Y. , Starodub E. , Kappes B. B. , Ciobanu C. V. , Bartelt N. C. , McCarty K. F. , 2010, Orientation-dependent work function of graphene on Pd(111), Applied Physics Letters, Vol. 97, No. 14DOI
25 
Hong S. M. , Kim H.-D. , An H.-M. , Kim T. G. , 2013, Effect of work function difference between top and bottom electrodes on the resistive switching properties of SiN films, IEEE Electron Device Letters, Vol. 34, No. 9, pp. 1181-1183DOI
26 
Bi J. , Guo Z. , Jiang K. , Sun J. , Xiang S. , Wang X. , Miao L. , Zhang T. , Zhang R. , Cao Y. , 2025, Determination of work function in single-crystalline and polycrystalline TiN films, The Journal of Physical Chemistry C, Vol. 129, No. 20, pp. 9580-9586DOI
27 
Liu S. , Dong S. , Jin H. , Huang S. , Wang X. , Luo J. , 2018, Significant effects of electrode metal work function on resistive memory devices with gelatin biodielectric layer, Journal of The Electrochemical Society, Vol. 165, No. 7, pp. G90-G95DOI
28 
Wang X. P. , Chen Y. Y. , Pantisano L. , Goux L. , Jurczak M. , Groeseneken G. , Wouters D. J. , 2010, Effect of anodic interface layers on the unipolar switching of HfO2-based resistive RAM, Proc. of the 2010 International Symposium on VLSI Technology, Systems and ApplicationsDOI
29 
Nell J. , O'Neill H. St. C. , 1996, Gibbs free energy of formation and heat capacity of PdO: A new calibration of the Pd-PdO buffer to high temperatures and pressures, Geochimica et Cosmochimica Acta, Vol. 60, No. 12, pp. 2411-2421DOI
30 
Ono L. K. , Hanafi M. M. , Cuenya B. R. , Rodriguez J. A. , 2002, Formation and thermal stability of Au2O3 on gold nanoparticles, Journal of Physical Chemistry B, Vol. 106, No. 35, pp. 9111-9117DOI
31 
Golosov D. A. , Okojie J. E. , Zavadski S. M. , Rudenkov A. S. , Melnikov S. N. , Kolos V. V. , 2018, Stability of the platinum electrode during high temperature annealing, Thin Solid Films, Vol. 661, pp. 53-59DOI
32 
Jain N. , Sharma S. K. , Kumawat R. , Jain P. K. , Kumar D. , Vyas R. , 2022, Resistive switching, endurance and retention properties of ZnO/HfO2 bilayer heterostructure memory device, Micro and Nano Structures, Vol. 169DOI
33 
Birks N. , Meier G. H. , Pettit F. S. , 2006, Introduction to the High Temperature Oxidation of MetalsDOI
34 
Ismail M. , Batool Z. , Mahmood K. , Rana A. M. , Yang B.-D. , Kim S. , 2020, Resistive switching characteristics and mechanism of bilayer HfO2/ZrO2 structure, Results in Physics, Vol. 18DOI
35 
Yeom C.-M. , Kumar D. , Eadi S. B. , Lee H. S. , Thallapally P. K. , Kwon H. M. , Fischer R. A. , Lee H. D. , Jayaramulu K. , 2024, ZnO@ZIF-8 heteronanostructures for advanced neuromorphic synaptic devices, Cell Reports Physical Science, Vol. 5, No. 10DOI
Yu-Bin Kim
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Yu-Bin Kim received her B.S. degree in electronic engineering from Chungnam National University, Daejeon, Korea, in 2025. She is currently pursuing an M.S. degree at the same university. Her research interests include RRAM-based neuromorphic devices, multi-layer memory structures, and device reliability.

Sung-Ho Kim
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Sung-Ho Kim received his B.S. degree in electronic engineering from Chungnam National University, Daejeon, Korea, in 2025. He is currently pursuing an M.S. degree at the same university. His research interests include RRAM-based neuromorphic devices, multi-layer memory structures, and device reliability.

Dong-Min Kim
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Dong-Min Kim received his B.S. degree in electronic engineering from Chungnam National University, Daejeon, Korea, in 2025. He is currently pursuing an M.S. degree at the same university. His research interests include RRAM-based neuromorphic devices, multi-layer memory structures, and device reliability.

Chae-Min Yeom
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Chae-Min Yeom received his B.S. and M.S. degrees in electronic engineering from Chungnam National University, Daejeon, Korea, in 2025. He is currently with DB HiTek, Eumseong, Korea, where he is involved in research on CMOS image sensor (CIS) technology. His research interests include semiconductor device design, CMOS image sensors, and circuit-device interaction.

Shivam Kumar Gautam
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Shivam Kumar Gautam received his B.Tech. degree from Bundelkhand Institute of Engineering Technology, Jhansi, India in 2014, an M.Tech. degree from Indian Institute of Technology (IIT) BHU, Varanasi, UP, India in 2016, and a Ph.D. degree from IIT Kanpur, India in 2023, all in Chemical Engineering. He was also associated with the SAMTEL Centre for Display Technologies at IIT Kanpur. From December 2023, he is a BK21 postdoctoral fellow at the department of Electronics Engineering at Chungnam National University, South Korea. He has been appointed as a contract professor at the same department at Chungnam National University in December 2024. His research interests include hybrid material based chemical and physical sensors for healthcare and environmental applications, 2D nanomaterial-based devices, and neuromorphic synaptic devices with silicon and flexible platforms.

Yong-Goo Kim
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Yong-Goo Kim received his B.S. degree in electrical and electronic engineering from Hongik University, Seoul, Korea, in 2002, and an M.S. degree in electronic engineering from Chungnam National University, Daejeon, Korea, in 2004. He completed a Ph.D. coursework in electronic engineering at Chungnam National University in 2008. He has held research positions at MagnaChip Semiconductor, the Electronics and Telecommunications Research Institute (ETRI), and LX Semicon, where he served as a Principal Engineer and Team Leader. Since 2022, he has been with the Department of Green Semiconductor System, Korea Polytechnic University, where he is currently a Professor. His research interests include low-power semiconductor design, emerging semiconductor devices, and semiconductor TEG/WLR.

Hyuk-Min Kwon
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Hyuk-Min Kwon received his B.S., M.S., and Ph.D. degrees from Chungnam National University, Daejeon, Korea, in 2007, 2009, and 2014, respectively, all in electronics engineering. He worked on non-Si and beyond structure development at SEMATECH, Austin, TX, USA, in 2013, and at Korea Advanced Nano Fab Center(KANC) in 2014. He worked for SK Hynix from 2014-2020 and for Korea Polytechnics College from 2020-2024. He joined the school of electronic and electrical engineering, Hankyong National University in 2024. His current research focuses on the development of new emerging memory and computing devices, as well as extreme low-power device technology. This includes innovative devices and their applications in integrated systems, such as edge computing and neuromorphic computing, as well as conventional technologies such as advanced gate stack technology and other high-performance logic device technologies.

Hi-Deok Lee
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Hi-Deok Lee received his B.S., M.S., and Ph.D. degrees in electrical engineering from the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 1990, 1992, and 1996, respectively. In 1993, he joined LG Semicon Company, Ltd. (currently SK hynix Semiconductor Ltd.), Cheongju, Korea, where he was involved in the development of 0.35 µm, 0.25 µm, and 0.18 µm CMOS technologies and was also responsible for the development of 0.15 µm and 0.13 µm CMOS technologies. Since 2001, he has been with the Department of Electronics Engineering, Chungnam National University, Daejeon, Korea. From 2006 to 2008, he was a Visiting Scholar with the University of Texas at Austin and SEMATECH, Austin. His research interests include nanoscale CMOS technology and its reliability physics, silicide technology, Test Element Group (TEG) design, high-performance analog and high-voltage MOSFETs, 2D nanomaterial-based devices, and neuromorphic devices.