PZT-Enabled MoS<sub>2</sub> Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing

Chen, Jing; Zhu, Ye-Qing; Zhao, Xue-Chun; Wang, Zheng-Hua; Zhang, Kai; Zhang, Zheng; Sun, Ming-Yuan; Wang, Shuai; Zhang, Yu; Han, Lin; Wu, Xiaoming; Ren, Tian-Ling

doi:10.1021/acs.nanolett.3c02721

Cited by 21 publications

(5 citation statements)

References 66 publications

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“…The ultrahigh operation speed of the PTT enables a single weight-update energy consumption as low as 0.0002 fJ for an artificial synapse. To further minimize the energy expenditure associated with weight-updating processes in the PTT, Hf 0.5 Zr 0.5 O 2 could be explored as an alternative to the PZT. , Moreover, the PTT technology can potentially be into scalable CMOS back-end-of-line-compatible artificial synapses, thanks to the mature wafer-level production processes of the PZT and MoS 2 . Furthermore, PTT-based artificial synapses have been used to construct an ANN for facial recognition.…”

Section: Discussionmentioning

confidence: 99%

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing

Chen,

Zhao,

Zhu

et al. 2023

ACS Nano

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Neural networks based on low-power artificial synapses can significantly reduce energy consumption, which is of great importance in today’s era of artificial intelligence. Two-dimensional (2D) material-based floating-gate transistors (FGTs) have emerged as compelling candidates for simulating artificial synapses owing to their multilevel and nonvolatile data storage capabilities. However, the low erasing/programming speed of FGTs renders them unsuitable for low-energy-consumption artificial synapses, thereby limiting their potential in high-energy-efficient neuromorphic computing. Here, we introduce a FGT-inspired MoS2/Trap/PZT heterostructure-based polarized tunneling transistor (PTT) with a simple fabrication process and significantly enhanced erasing/programming speed. Distinct from the FGT, the PTT lacks a tunnel layer, leading to a marked improvement in its erasing/programming speed. The PTT’s highest erasing/programming (operation) speed can reach ∼20 ns, which outperforms the performance of most FGTs based on 2D heterostructures. Furthermore, the PTT has been utilized as an artificial synapse, and its weight-update energy consumption can be as low as 0.0002 femtojoule (fJ), which benefits from the PTT’s ultrahigh operation speed. Additionally, PTT-based artificial synapses have been employed in constructing artificial neural network simulations, achieving facial-recognition accuracy (95%). This groundbreaking work makes it possible for fabricating future high-energy-efficient neuromorphic transistors utilizing 2D materials.

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Section: Discussionmentioning

confidence: 99%

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing

Chen,

Zhao,

Zhu

et al. 2023

ACS Nano

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“…FeFET's memory characteristics can mimic artificial synapses by adjusting the polarization magnitude with pulses to map different weights. This reveals its extraordinary potential in low-power neuromorphic computing applications [614]. Neuromorphic computing based on FeCT has also been explored [615].…”

Section: The Application Of 2d Memory In Neuromorphic Computingmentioning

confidence: 99%

Two-dimensional materials for future information technology: status and prospects

Qiu,

Yu,

Zhao

et al. 2024

Sci. China Inf. Sci.

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Over the past 70 years, the semiconductor industry has undergone transformative changes, largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional (2D) materials like transition metal dichalcogenides (TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

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“…In the aspect of perception recognition based on in-sensor neuromorphic computing, single-device computing is the basic content of current research [259][260][261][262][263][264][265][266][267][268][269]. The artificial synapse based on a single device can realize the recognition of partial picture information including handwriting through simulation.…”

Section: Sensory Perceptionmentioning

confidence: 99%

In-sensor neuromorphic computing using perovskites and transition metal dichalcogenides

Li,

Zhou

et al. 2024

J. Phys. Mater.

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With the advancements in Web of Things, Artificial Intelligence, and other emerging technologies, there is an increasing demand for artificial visual systems to perceive and learn about external environments. However, traditional sensing and computing systems are limited by the physical separation of sense, processing, and memory units that results in the challenges such as high energy consumption, large additional hardware costs, and long latency time. Integrating neuromorphic computing functions into the sensing unit is an effective way to overcome these challenges. Therefore, it is extremely important to design neuromorphic devices with sensing ability and the properties of low power consumption and high switching speed for exploring in-sensor computing devices and systems. In this review, we provide an elementary introduction to the structures and properties of two common optoelectronic materials, perovskites and transition metal dichalcogenides (TMDs). Subsequently, we discuss the fundamental concepts of neuromorphic devices, including device structures and working mechanisms. Furthermore, we summarize and extensively discuss the applications of perovskites and TMDs in in-sensor computing. Finally, we propose potential strategies to address challenges and offer a brief outlook on the application of optoelectronic materials in term of in-sensor computing.

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PZT-Enabled MoS₂ Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing

Cited by 21 publications

References 66 publications

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing

Two-dimensional materials for future information technology: status and prospects

In-sensor neuromorphic computing using perovskites and transition metal dichalcogenides

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PZT-Enabled MoS2 Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing

Cited by 21 publications

References 66 publications

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing

Two-dimensional materials for future information technology: status and prospects

In-sensor neuromorphic computing using perovskites and transition metal dichalcogenides

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Product

Resources

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PZT-Enabled MoS₂ Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing