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Articles by Wonjun Shin
Antimony-contact MoS 2 FET gas sensors for reinforcement-learning–driven hazard perception at room temperature
Abstract Two-dimensional van der Waals semiconductors promise low-power chemical perception at room temperature, yet contact-limited on-currents and slow recovery impede closed-loop decision making in hazardous environments.
Defect-mediated generation–recombination dynamics governing conductance response in floating-body transistors
Abstract As CMOS technologies evolve toward ultra-thin floating-body architectures, defect-mediated carrier dynamics increasingly dominate device behavior, challenging conventional linear interpretations of electrical response in nanoscale transistors. In this work, we demonstrate that the conductance response of foundry-fabricated floating-body transistors is governed by generation–recombination (GR) dynamics associated with deep-level defects inside the ultra-thin silicon body.
A ferroelectric-ionic-trapping transistor for low power and secure neuromorphic computing
Abstract The convergence of artificial intelligence and pervasive data analytics has created an urgent demand for energy-efficient and secure computing hardware. Neuromorphic synaptic devices emulate the human brain with high parallelism and 3D connectivity to reduce power consumption, yet they lack intrinsic mechanisms to protect stored information from malicious read-out.
Hybrid ferroelectric-ionic memristive hardware for high scalability in-memory computing
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Abstract In-memory computing using two-terminal memristors offers a promising route to reduce the energy demands of data-intensive computing. However, current devices scale poorly due to sneak currents and materials that are incompatible with standard complementary metal-oxide-semiconductor and very large-scale integration processes.
By Jeong-Han Kim, Wonjun Shin, Ryun-Han Koo, Jangsaeng Kim, Eugene Park, Piush Behera, Sojin Kim, Jinseok Hong, Feras Al-Dirini, Been Kwak, Jiwon You, Jiseong Im, Qinyuan Xue, Hyun-Min Kim, Hyunho Seok, Youngchan Cho, Hwiin Ju, Kyunghwan Lee, Daewon Ha, Jong-Ho Lee, Seung-Yong Lee, Deok-Hwang Kwon, Frances M. Ross, Youngho Kang, Suraj S. Cheema, Daewoong Kwon, Dooyong Koh, Yejin Hong, Wooje Jung
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Nature
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Thermally activated excess noise by subgap density-of-states in Si-doped ZnSnO thin-film transistor-type gas sensor
Abstract Amorphous oxide semiconductor (AOS) thin-film transistor (TFT) platforms have shown exceptional promise for advanced gas-sensing applications due to their intrinsic sensitivity, tunable electrical properties, and suitability for integration with flexible substrates.
Sub-mV tunable photonic p-bits for probabilistic computing
Abstract INTRODUCTION RESULTS DISCUSSION MATERIALS AND METHODS Acknowledgments Supplementary Materials REFERENCES Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Randomness, once dismissed as unwanted noise, is now emerging as a foundation for intelligent computation.
Unraveling ionic switching dynamics in high-k dielectric double-gate transistors via low-frequency noise spectroscopy - Nano Convergence
References J. Robertson, High dielectric constant oxides. EPJ Appl. Phys. 28, 265 (2004). https://doi.org/10.1051/epjap:2004206 Article CAS Google Scholar G.D. Wilk, R.M. Wallace, J.M. Anthony, High-κ gate dielectrics: current status and materials properties considerations. J. Appl. Phys. 89, 5243 (2001). https://doi.org/10.1063/1.1361065 Article CAS Google Scholar J.A. Kittl, K. Opsomer, M. Popovici, N. Menou, B. Kaczer, X.P. Wang, C. Adelmann, M.A. Pawlak, K. Tomida, A. Rothschild, B. Govoreanu, R. Degraeve, M.
Physical correlation between stochasticity and process-induced damage in ferroelectric memory devices - Nano Convergence
As the semiconductor industry pushes device miniaturization and performance to unprecedented levels, extreme manufacturing environments—characterized by high temperatures, large mechanical stresses, and aggressive plasma-based deposition processes—have become integral to the fabrication of next-generation electronics [1,2,3].
Ferroelectric NAND for efficient hardware bayesian neural networks - Nature Communications
Abstract The rapid advancement of artificial intelligence has enabled breakthroughs in diverse fields, including autonomous systems and medical diagnostics. However, conventional deterministic neural networks struggle to capture uncertainty, limiting their reliability when handling real-world data, which are often noisy, imbalanced, or scarce.
A New Back‐End‐Of‐Line Ferroelectric Field‐Effect Transistor Platform via Laser Processing (Small 15/2025)
Suraj S Cheema Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139 USA Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139 USA Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139 USA Search for more papers by this author
Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A Review
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Analog reservoir computing via ferroelectric mixed phase boundary transistors - Nature Communications
Abstract Analog reservoir computing (ARC) systems have attracted attention owing to their efficiency in processing temporal information. However, the distinct functionalities of the system components pose challenges for hardware implementation. Herein, we report a fully integrated ARC system that leverages material versatility of the ferroelectric-to-mixed phase boundary (MPB) hafnium zirconium oxides integrated onto indium–gallium–zinc oxide thin-film transistors (TFTs).
All‐Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons
1 Introduction The advent of deep neural networks has marked a significant milestone in computational technology.[1-6] However, these systems often rely on the conventional von Neumann computing architecture, which suffers from critical limitations in area and energy efficiency.[7-12] Neuromorphic computing architecture inspired by the biological mechanisms of the human nervous system has been explored as a potential approach for realizing more efficient computing paradigms.[13-15] Spiking...
Polarization Pruning: Reliability Enhancement of Hafnia‐Based Ferroelectric Devices for Memory and Neuromorphic Computing
1 Introduction The advancement of future electronics beyond Moore's paradigm requires the introduction of novel materials that exploit diverse functionalities and methods to control them at different scalable dimensions.[1, 2] Ferroelectricity, which is characterized by the spontaneous polarization of materials that can be controlled by an external electric (E)-field is a promising avenue for such exploration.[3-24] Of particular interest is the emergence of hafnia-based ferroelectrics, which...
Toward Ideal Low‐Frequency Noise in Monolayer CVD MoS2 FETs: Influence of van der Waals Junctions and Sulfur Vacancy Management
1 Introduction The relentless scaling of silicon complementary metal–oxide–semiconductor technology has successfully reached sub-10-nm nodes. However, further scaling has become challenging due to the gate electrostatics of the devices.[1, 2] To maintain the desired performance, an aggressive reduction in channel thickness is necessary. The ultimate goal is to achieve a field-effect transistor (FET) channel thickness in the sub-1-nm range.
Demonstration of In‐Memory Biosignal Analysis: Novel High‐Density and Low‐Power 3D Flash Memory Array for Arrhythmia Detection
1 Introduction Cardiovascular diseases (CVDs) constitute a significant portion of mortalities worldwide.[1-4] Timely identification is paramount in mitigating or managing the progression of CVDs effectively. Cardiac arrhythmias commonly serve as indicators for various CVDs. Since the arrhythmias are manifested in the electrical activities of the heart, electrocardiogram (ECG) signals prove invaluable in their detection.
Charge Transport Advancement in Anti‐Ambipolar Transistors: Spatially Separating Layer Sandwiched between N‐Type Metal Oxides and P‐Type Small Molecules
Conflict of Interest The authors declare no conflict of interest. Supporting Information Filename Description adfm202316217-sup-0001-SuppMat.pdf5.3 MB Supporting Information References 1, , , Adv. Sci. 2021, 8, 2004216. 2, , , , , , , , , , Adv. Funct. Mater. 2019, 29, 1905540. 3, , , , , , , , , Adv. Mater. Interfaces 2020, 7, 2000893. 4, , , , , , , , , , Nano Lett. 2020, 20, 2907. 5, , , , , , , , , , Sci. Rep. 2021, 11, 3688. 6, , , , , , , Adv. Funct. Mater. 2020, 30, 1909645. 7, , , Adv. Funct. Mater.
Proposition of Adaptive Read Bias: A Solution to Overcome Power and Scaling Limitations in Ferroelectric‐Based Neuromorphic System
1 Introduction The von Neumann architecture, which has served as the foundation of computing architecture for decades, is countering challenges owing to the increasing prevalence of data-centric cognitive tasks such as computer vision, natural language processing, and autonomous driving.[1-3] These data-centric cognitive tasks require frequent and inefficient transfers between the processor and memory, which consume significant time and power.
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