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Tan D, Sun N, Huang J, Zhang Z, Zeng L, Li Q, Bi S, Bu J, Peng Y, Guo Q, Jiang C. Monolayer Vacancy-Induced MXene Memory for Write-Verify-Free Programming. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2402273. [PMID: 38682587 DOI: 10.1002/smll.202402273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/17/2024] [Indexed: 05/01/2024]
Abstract
The fundamental logic states of 1 and 0 in Complementary Metal-Oxide-Semiconductor (CMOS) are essential for modern high-speed non-volatile solid-state memories. However, the accumulated storage signal in conventional physical components often leads to data distortion after multiple write operations. This necessitates a write-verify operation to ensure proper values within the 0/1 threshold ranges. In this work, a non-gradual switching memory with two distinct stable resistance levels is introduced, enabled by the asymmetric vertical structure of monolayer vacancy-induced oxidized Ti3C2Tx MXene for efficient carrier trapping and releasing. This non-cumulative resistance effect allows non-volatile memories to attain valid 0/1 logic levels through direct reprogramming, eliminating the need for a write-verify operation. The device exhibits superior performance characteristics, including short write/erase times (100 ns), a large switching ratio (≈3 × 104), long cyclic endurance (>104 cycles), extended retention (>4 × 106 s), and highly resistive stability (>104 continuous write operations). These findings present promising avenues for next-generation resistive memories, offering faster programming speed, exceptional write performance, and streamlined algorithms.
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Affiliation(s)
- Dongchen Tan
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Nan Sun
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Jijie Huang
- School of Materials Engineering, Purdue University, West Lafayette, 47907, USA
| | - Zhaorui Zhang
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Lijun Zeng
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Qikun Li
- School of Advanced Materials and Nanotechnology, Xidian University, Xi'an, 710126, China
| | - Sheng Bi
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Jingyuan Bu
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Yan Peng
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Qinlei Guo
- Department of Material Science and Engineering, Frederick Seitz Material Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, 61801, USA
| | - Chengming Jiang
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
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Shim SK, Jang YH, Han J, Jeon JW, Shin DH, Kim YR, Han JK, Woo KS, Lee SH, Cheong S, Kim J, Seo H, Shin J, Hwang CS. 2Memristor-1Capacitor Integrated Temporal Kernel for High-Dimensional Data Mapping. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2306585. [PMID: 38212281 DOI: 10.1002/smll.202306585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/01/2023] [Indexed: 01/13/2024]
Abstract
Compact but precise feature-extracting ability is core to processing complex computational tasks in neuromorphic hardware. Physical reservoir computing (RC) offers a robust framework to map temporal data into a high-dimensional space using the time dynamics of a material system, such as a volatile memristor. However, conventional physical RC systems have limited dynamics for the given material properties, restricting the methods to increase their dimensionality. This study proposes an integrated temporal kernel composed of a 2-memristor and 1-capacitor (2M1C) using a W/HfO2/TiN memristor and TiN/ZrO2/Al2O3/ZrO2/TiN capacitor to achieve higher dimensionality and tunable dynamics. The kernel elements are carefully designed and fabricated into an integrated array, of which performances are evaluated under diverse conditions. By optimizing the time dynamics of the 2M1C kernel, each memristor simultaneously extracts complementary information from input signals. The MNIST benchmark digit classification task achieves a high accuracy of 94.3% with a (196×10) single-layer network. Analog input mapping ability is tested with a Mackey-Glass time series prediction, and the system records a normalized root mean square error of 0.04 with a 20×1 readout network, the smallest readout network ever used for Mackey-Glass prediction in RC. These performances demonstrate its high potential for efficient temporal data analysis.
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Affiliation(s)
- Sung Keun Shim
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Yoon Ho Jang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Janguk Han
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jeong Woo Jeon
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Dong Hoon Shin
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Yeong Rok Kim
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Joon-Kyu Han
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Kyung Seok Woo
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Soo Hyung Lee
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sunwoo Cheong
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jaehyun Kim
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Haengha Seo
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jonghoon Shin
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
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3
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Jeon K, Ryu JJ, Im S, Seo HK, Eom T, Ju H, Yang MK, Jeong DS, Kim GH. Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators. Nat Commun 2024; 15:129. [PMID: 38167379 PMCID: PMC10761713 DOI: 10.1038/s41467-023-44620-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
Memristor-integrated passive crossbar arrays (CAs) could potentially accelerate neural network (NN) computations, but studies on these devices are limited to software-based simulations owing to their poor reliability. Herein, we propose a self-rectifying memristor-based 1 kb CA as a hardware accelerator for NN computations. We conducted fully hardware-based single-layer NN classification tasks involving the Modified National Institute of Standards and Technology database using the developed passive CA, and achieved 100% classification accuracy for 1500 test sets. We also investigated the influences of the defect-tolerance capability of the CA, impact of the conductance range of the integrated memristors, and presence or absence of selection functionality in the integrated memristors on the image classification tasks. We offer valuable insights into the behavior and performance of CA devices under various conditions and provide evidence of the practicality of memristor-integrated passive CAs as hardware accelerators for NN applications.
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Affiliation(s)
- Kanghyeok Jeon
- Division of Materials Science and Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Division of Advanced Materials, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea
| | - Jin Joo Ryu
- Division of Advanced Materials, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea
- Department of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Seongil Im
- Center for Opto-Electronic Materials and Devices, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Hyun Kyu Seo
- Intelligent Electronic Device Lab, Sahmyook University, 815 Hwarang-ro, Nowon-Gu, Seoul, 01795, Republic of Korea
| | - Taeyong Eom
- Division of Advanced Materials, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea
| | - Hyunsu Ju
- Center for Opto-Electronic Materials and Devices, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea.
| | - Min Kyu Yang
- Intelligent Electronic Device Lab, Sahmyook University, 815 Hwarang-ro, Nowon-Gu, Seoul, 01795, Republic of Korea.
| | - Doo Seok Jeong
- Division of Materials Science and Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
| | - Gun Hwan Kim
- Department of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
- Department of System Semiconductor Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
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Hellenbrand M, MacManus-Driscoll J. Multi-level resistive switching in hafnium-oxide-based devices for neuromorphic computing. NANO CONVERGENCE 2023; 10:44. [PMID: 37710080 PMCID: PMC10501996 DOI: 10.1186/s40580-023-00392-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023]
Abstract
In the growing area of neuromorphic and in-memory computing, there are multiple reviews available. Most of them cover a broad range of topics, which naturally comes at the cost of details in specific areas. Here, we address the specific area of multi-level resistive switching in hafnium-oxide-based devices for neuromorphic applications and summarize the progress of the most recent years. While the general approach of resistive switching based on hafnium oxide thin films has been very busy over the last decade or so, the development of hafnium oxide with a continuous range of programmable states per device is still at a very early stage and demonstrations are mostly at the level of individual devices with limited data provided. On the other hand, it is positive that there are a few demonstrations of full network implementations. We summarize the general status of the field, point out open questions, and provide recommendations for future work.
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Affiliation(s)
- Markus Hellenbrand
- Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge, CB3 0FS, UK.
| | - Judith MacManus-Driscoll
- Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge, CB3 0FS, UK
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Hellenbrand M, Bakhit B, Dou H, Xiao M, Hill MO, Sun Z, Mehonic A, Chen A, Jia Q, Wang H, MacManus-Driscoll JL. Thin-film design of amorphous hafnium oxide nanocomposites enabling strong interfacial resistive switching uniformity. SCIENCE ADVANCES 2023; 9:eadg1946. [PMID: 37343094 DOI: 10.1126/sciadv.adg1946] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 05/16/2023] [Indexed: 06/23/2023]
Abstract
A design concept of phase-separated amorphous nanocomposite thin films is presented that realizes interfacial resistive switching (RS) in hafnium oxide-based devices. The films are formed by incorporating an average of 7% Ba into hafnium oxide during pulsed laser deposition at temperatures ≤400°C. The added Ba prevents the films from crystallizing and leads to ∼20-nm-thin films consisting of an amorphous HfOx host matrix interspersed with ∼2-nm-wide, ∼5-to-10-nm-pitch Ba-rich amorphous nanocolumns penetrating approximately two-thirds through the films. This restricts the RS to an interfacial Schottky-like energy barrier whose magnitude is tuned by ionic migration under an applied electric field. Resulting devices achieve stable cycle-to-cycle, device-to-device, and sample-to-sample reproducibility with a measured switching endurance of ≥104 cycles for a memory window ≥10 at switching voltages of ±2 V. Each device can be set to multiple intermediate resistance states, which enables synaptic spike-timing-dependent plasticity. The presented concept unlocks additional design variables for RS devices.
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Affiliation(s)
- Markus Hellenbrand
- Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge CB3 0FS, UK
| | - Babak Bakhit
- Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge CB3 0FS, UK
- Department of Engineering, University of Cambridge, 9 JJ Thompson Avenue, Cambridge CB3 0FA, UK
- Department of Physics, Linköping University Thin Film Physics Division, 581 83 Linköping, Sweden
| | - Hongyi Dou
- School of Materials Engineering, Purdue University, Neil Armstrong Hall of Engineering, 701 West Stadium Avenue, West Lafayette, IN 47907-2045, USA
| | - Ming Xiao
- Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge CB3 0FS, UK
| | - Megan O Hill
- Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge CB3 0FS, UK
| | - Zhuotong Sun
- Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge CB3 0FS, UK
| | - Adnan Mehonic
- Department of Electronic & Electrical Engineering, University College London, London WC1E 7JE, UK
| | - Aiping Chen
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Quanxi Jia
- Department of Materials Design and Innovation, University at Buffalo, 136 Bell Hall, Buffalo, NY 14260, USA
| | - Haiyan Wang
- School of Materials Engineering, Purdue University, Neil Armstrong Hall of Engineering, 701 West Stadium Avenue, West Lafayette, IN 47907-2045, USA
| | - Judith L MacManus-Driscoll
- Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge CB3 0FS, UK
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Jang YH, Kim W, Kim J, Woo KS, Lee HJ, Jeon JW, Shim SK, Han J, Hwang CS. Time-varying data processing with nonvolatile memristor-based temporal kernel. Nat Commun 2021; 12:5727. [PMID: 34593800 PMCID: PMC8484437 DOI: 10.1038/s41467-021-25925-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 09/09/2021] [Indexed: 11/24/2022] Open
Abstract
Recent advances in physical reservoir computing, which is a type of temporal kernel, have made it possible to perform complicated timing-related tasks using a linear classifier. However, the fixed reservoir dynamics in previous studies have limited application fields. In this study, temporal kernel computing was implemented with a physical kernel that consisted of a W/HfO2/TiN memristor, a capacitor, and a resistor, in which the kernel dynamics could be arbitrarily controlled by changing the circuit parameters. After the capability of the temporal kernel to identify the static MNIST data was proven, the system was adopted to recognize the sequential data, ultrasound (malignancy of lesions) and electrocardiogram (arrhythmia), that had a significantly different time constant (10-7 vs. 1 s). The suggested system feasibly performed the tasks by simply varying the capacitance and resistance. These functionalities demonstrate the high adaptability of the present temporal kernel compared to the previous ones.
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Affiliation(s)
- Yoon Ho Jang
- Department of Materials Science and Engineering College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Woohyun Kim
- Department of Materials Science and Engineering College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jihun Kim
- Department of Materials Science and Engineering College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Kyung Seok Woo
- Department of Materials Science and Engineering College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Hyun Jae Lee
- Department of Materials Science and Engineering College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jeong Woo Jeon
- Department of Materials Science and Engineering College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sung Keun Shim
- Department of Materials Science and Engineering College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Janguk Han
- Department of Materials Science and Engineering College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea.
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Jeon K, Kim J, Ryu JJ, Yoo SJ, Song C, Yang MK, Jeong DS, Kim GH. Self-rectifying resistive memory in passive crossbar arrays. Nat Commun 2021; 12:2968. [PMID: 34016978 PMCID: PMC8137934 DOI: 10.1038/s41467-021-23180-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 04/16/2021] [Indexed: 11/09/2022] Open
Abstract
Conventional computing architectures are poor suited to the unique workload demands of deep learning, which has led to a surge in interest in memory-centric computing. Herein, a trilayer (Hf0.8Si0.2O2/Al2O3/Hf0.5Si0.5O2)-based self-rectifying resistive memory cell (SRMC) that exhibits (i) large selectivity (ca. 104), (ii) two-bit operation, (iii) low read power (4 and 0.8 nW for low and high resistance states, respectively), (iv) read latency (<10 μs), (v) excellent non-volatility (data retention >104 s at 85 °C), and (vi) complementary metal-oxide-semiconductor compatibility (maximum supply voltage ≤5 V) is introduced, which outperforms previously reported SRMCs. These characteristics render the SRMC highly suitable for the main memory for memory-centric computing which can improve deep learning acceleration. Furthermore, the low programming power (ca. 18 nW), latency (100 μs), and endurance (>106) highlight the energy-efficiency and highly reliable random-access memory of our SRMC. The feasible operation of individual SRMCs in passive crossbar arrays of different sizes (30 × 30, 160 × 160, and 320 × 320) is attributed to the large asymmetry and nonlinearity in the current-voltage behavior of the proposed SRMC, verifying its potential for application in large-scale and high-density non-volatile memory for memory-centric computing.
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Affiliation(s)
- Kanghyeok Jeon
- Division of Advanced Materials, Korea Research Institute of Chemical Technology (KRICT) 141 Gajeong-Ro, Yuseong-Gu, Daejeon, Republic of Korea
- Division of Materials Science and Engineering, Hanyang University, Seoul, Republic of Korea
| | - Jeeson Kim
- Division of Materials Science and Engineering, Hanyang University, Seoul, Republic of Korea
| | - Jin Joo Ryu
- Division of Advanced Materials, Korea Research Institute of Chemical Technology (KRICT) 141 Gajeong-Ro, Yuseong-Gu, Daejeon, Republic of Korea
| | - Seung-Jong Yoo
- Division of Advanced Materials, Korea Research Institute of Chemical Technology (KRICT) 141 Gajeong-Ro, Yuseong-Gu, Daejeon, Republic of Korea
- Division of Materials Science and Engineering, Hanyang University, Seoul, Republic of Korea
| | - Choongseok Song
- Division of Materials Science and Engineering, Hanyang University, Seoul, Republic of Korea
| | - Min Kyu Yang
- Intelligent Electronic Device Lab, Sahmyook University, Seoul, Republic of Korea
| | - Doo Seok Jeong
- Division of Materials Science and Engineering, Hanyang University, Seoul, Republic of Korea.
| | - Gun Hwan Kim
- Division of Advanced Materials, Korea Research Institute of Chemical Technology (KRICT) 141 Gajeong-Ro, Yuseong-Gu, Daejeon, Republic of Korea.
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