Yufei Shi

National University of Singapore

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Kah‐Wee Ang

National University of Singapore

Yu‐Chieh Chien

National University of Singapore

Heng Xiang

National University of Singapore

Ngoc Thanh Duong

Sungkyunkwan University

Haofei Zheng

National University of Singapore

Sifan Li

National University of Singapore

Lingqi Li

Qingdao University

Sifan Li

Cultural Relics Institute Hebei Province

Jianze Wang

Sichuan University

Xuanyao Fong

National University of Singapore

Cooperative Institutions

National University of Singapore

Southern University of Science and Technology

Agency for Science, Technology and Research

Beijing University of Agriculture

Institute of Materials Research and Engineering

Tsinghua University

Liaoning Normal University

Harbin Institute of Technology

Peking University

Institute of Microelectronics

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Arduino-based Intelligent Security Cart Design and Implementation

Yufei Shi Xiaodong Zhu Yan Yang H. Liu

Cart

Arduino

10.1145/3679409.3679490

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Machine Learning Attack Resilient MoS2 Fe-FET True Random Number Generator for Hardware Security in IoT: 0.7 pJ/bit Writing Energy, Self-Correction Function, and 1250 bit/s Seed Throughput

Extended Abstracts of the 2020 International Conference on Solid State Devices and Materials (2022)

Yu‐Chieh Chien Heng Xiang Jianze Wang Yufei Shi Xuanyao Fong

Bit (key)

10.7567/ssdm.2022.f-9-03

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First Demonstration of Bi-directional Sub-50 mV/dec MoS 2-Hf 0.5Zr 0.5O 2 Ferroelectric FET with Record High Memory Window Efficiency Exceeding the Theoretical Limit and High-Precision Analog Synapse

Extended Abstracts of the 2020 International Conference on Solid State Devices and Materials (2023)

Heng Xiang Yu‐Chieh Chien Lingqi Li Haofei Zheng Sifan Li

10.7567/ssdm.2023.a-6-02

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Emerging 2D Materials Hardware for In-sensor Computing

Nanoscale Horizons (2024)

Yufei Shi Ngoc Thanh Duong Kah‐Wee Ang

This review covers recent advancements and future directions in 2DM-based devices for in-sensor computing, focusing on unique physical mechanisms for sensory responses, biomimetic synaptic features, and potential applications.

10.1039/d4nh00405a

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Citations (1)

Enhancing Memory Window Efficiency of Ferroelectric Transistor for Neuromorphic Computing via Two‐Dimensional Materials Integration

Advanced Functional Materials (2023)

Heng Xiang Yu‐Chieh Chien Lingqi Li Haofei Zheng Sifan Li

Abstract In‐memory computing, particularly neuromorphic computing, has emerged as a promising solution to overcome the energy and time‐consuming challenges associated with the von Neumann architecture. The ferroelectric field‐effect transistor (FeFET) technology, with its fast and energy‐efficient switching and nonvolatile memory, is a potential candidate for enabling both computing and memory within a single transistor. In this study, the capabilities of an integrated ferroelectric HfO 2 and 2D MoS 2 channel FeFET in achieving high‐performance 4‐bit per cell memory with low variation and power consumption synapses, while retaining the ability to implement diverse learning rules, are demonstrated. Notably, this device accurately recognizes MNIST handwritten digits with over 94% accuracy using online training mode. These results highlight the potential of FeFET‐based in‐memory computing for future neuromorphic computing applications.

Neuromorphic engineering

MNIST database

Non-Volatile Memory

In-Memory Processing

10.1002/adfm.202304657

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Citations (25)

Application of 2D Materials in Hardware Security for Internet‐of‐Things: Progress and Perspective

Small Structures (2022)

Heng Xiang Yu‐Chieh Chien Yufei Shi Kah‐Wee Ang

Internet‐of‐Things (IoT) is a ubiquitous network that features a tremendous amount of data and myriads of heterogeneous devices, which are interconnected and accessible or controllable anywhere and anytime. The security of IoT is therefore unequivocally crucial in several aspects, such as device‐to‐device communication, sensing and actuating, and information exchange. Conventional cryptographic algorithms and silicon‐based security primitives are constantly challenged by evolving methods of attack. By far, many efforts and achievements have been made using 2D materials for various electronics applications. Therefore, it is plausible to explore the implementation of hardware security using 2D materials, for example, true random number generators (TRNGs), physical unclonable functions (PUFs), camouflage, and anticounterfeit. TRNGs and PUFs are critical elements of hardware security and are widely deployed in cryptographic keys, identification, and authentication. In contrast to conventional utilization of manufacturing variations, security primitives using 2D materials have other entropy sources to exploit, such as the random nature of material growth and intrinsic randomness in charge trapping/detrapping. In this review, research progresses in 2D material‐based TRNGs, PUFs, and other security applications are summarized, along with the discussion on entropy sources, reliability, circuit, and machine learning modeling attacks launched on TRNGs and PUFs.

Hardware security module

Physical unclonable function

Cryptographic nonce

10.1002/sstr.202200060

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A MoS ₂ Hafnium Oxide Based Ferroelectric Encoder for Temporal‐Efficient Spiking Neural Network

Advanced Materials (2022)

Yu‐Chieh Chien Heng Xiang Yufei Shi Ngoc Thanh Duong Sifan Li

Spiking neural network (SNN), where the information is evaluated recurrently through spikes, has manifested significant promises to minimize the energy expenditure in data-intensive machine learning and artificial intelligence. Among these applications, the artificial neural encoders are essential to convert the external stimuli to a spiking format that can be subsequently fed to the neural network. Here, a molybdenum disulfide (MoS2 ) hafnium oxide-based ferroelectric encoder is demonstrated for temporal-efficient information processing in SNN. The fast domain switching attribute associated with the polycrystalline nature of hafnium oxide-based ferroelectric material is exploited for spike encoding, rendering it suitable for realizing biomimetic encoders. Accordingly, a high-performance ferroelectric encoder is achieved, featuring a superior switching efficiency, negligible charge trapping effect, and robust ferroelectric response, which successfully enable a broad dynamic range. Furthermore, an SNN is simulated to verify the precision of the encoded information, in which an average inference accuracy of 95.14% can be achieved, using the Modified National Insitute of Standards and Technology (MNIST) dataset for digit classification. Moreover, this ferroelectric encoder manifests prominent resilience against noise injection with an overall prediction accuracy of 94.73% under various Gaussian noise levels, showing practical promises to reduce the computational load for the neural network.

Neuromorphic engineering

MNIST database

10.1002/adma.202204949

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Dynamic Ferroelectric Transistor‐Based Reservoir Computing for Spatiotemporal Information Processing

Advanced Intelligent Systems (2023)

Ngoc Thanh Duong Yu‐Chieh Chien Heng Xiang Sifan Li Haofei Zheng

Reservoir computing (RC) architecture which mimics the human brain is a fundamentally preferred method to process dynamical systems that evolve with time. However, the difficulty in generating rich reservoir states using two‐terminal devices remains challenging, which hinders its hardware implementation. Herein, the 1D array of ferroelectric field‐effect transistor (Fe‐FET) based on α ‐In 2 Se 3 channel, which shows volatile memory effect for realizing various RC systems, is demonstrated. The fading effect in α ‐In 2 Se 3 is sufficiently investigated by polarization dynamic model. The proposed Fe‐FET is capable of experimentally classifying images using MNIST dataset with a high accuracy of 91%. Furthermore, time‐series real‐life chaotic system, for example, Earth's weather, can be accurately forecasted using our Ferro‐RC based on the Jena climate dataset recorded in a 1 year period. Remarkable determination coefficient ( R 2 ) of 0.9983 and normalized root mean square error (NRMSE) of 8.3 × 10 −3 are achieved using a minimized readout network. The demonstration of integrated memory and computation opens a route for realizing a compact RC hardware system.

Reservoir computing

MNIST database

10.1002/aisy.202300009

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Citations (19)

Attack Resilient True Random Number Generators Using Ferroelectric‐Enhanced Stochasticity in 2D Transistor

Small (2023)

Yu‐Chieh Chien Heng Xiang Jianze Wang Yufei Shi Xuanyao Fong

By harnessing the physically unclonable properties, true random number generators (TRNGs) offer significant promises to alleviate security concerns by generating random bitstreams that are cryptographically secured. However, fundamental challenges remain as conventional hardware often requires complex circuitry design, showing a predictable pattern that is susceptible to machine learning attacks. Here, a low-power self-corrected TRNG is presented by exploiting the stochastic ferroelectric switching and charge trapping in molybdenum disulfide (MoS2 ) ferroelectric field-effect transistors (Fe-FET) based on hafnium oxide complex. The proposed TRNG exhibits enhanced stochastic variability with near-ideal entropy of ≈1.0, Hamming distance of ≈50%, independent autocorrelation function, and reliable endurance cycle against temperature variations. Furthermore, its unpredictable feature is systematically examined by machine learning attacks, namely the predictive regression model and the long-short-term-memory (LSTM) approach, where nondeterministic predictions can be concluded. Moreover, the generated cryptographic keys from the circuitry successfully pass the National Institute of Standards and Technology (NIST) 800-20 statistical test suite. The potential of integrating ferroelectric and 2D materials is highlighted for advanced data encryption, offering a novel alternative to generate truly random numbers.

NIST

Physical unclonable function

10.1002/smll.202302842

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Citations (6)

Coupled Ferroelectric‐Photonic Memory in a Retinomorphic Hardware for In‐Sensor Computing

Advanced Science (2024)

Ngoc Thanh Duong Yufei Shi Sifan Li Yu‐Chieh Chien Heng Xiang

Abstract The development of all‐in‐one devices for artificial visual systems offers an attractive solution in terms of energy efficiency and real‐time processing speed. In recent years, the proliferation of smart sensors in the growth of Internet‐of‐Things (IoT) has led to the increasing importance of in‐sensor computing technology, which places computational power at the edge of the data‐flow architecture. In this study, a prototype visual sensor inspired by the human retina is proposed, which integrates ferroelectricity and photosensitivity in two‐dimensional (2D) α‐In 2 Se 3 material. This device mimics the functions of photoreceptors and amacrine cells in the retina, performing optical reception and memory computation functions through the use of electrical switching polarization in the channel. The gate‐tunable linearity of excitatory and inhibitory functions in photon‐induced short‐term plasticity enables to encode and classify 12 000 images in the Mixed National Institute of Standards and Technology (MNIST) dataset with remarkable accuracy, achieving ≈94%. Additionally, in‐sensor convolution image processing through a network of phototransistors, with five convolutional kernels electrically pre‐programmed into the transistors is demonstrated. The convoluted photocurrent matrices undergo straightforward arithmetic calculations to produce edge and feature‐enhanced scenarios. The findings demonstrate the potential of ferroelectric α‐In 2 Se 3 for highly compact and efficient retinomorphic hardware implementation, regardless of ambipolar transport in the channel.

Neuromorphic engineering

10.1002/advs.202303447

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Citations (9)