Wen-Kuan Yeh

Bio: Wen-Kuan Yeh is an academic researcher from National University of Kaohsiung. The author has contributed to research in topics: Silicon on insulator & Field-effect transistor. The author has an hindex of 19, co-authored 193 publications receiving 1489 citations. Previous affiliations of Wen-Kuan Yeh include Hodges University.

Sub-60mV-swing negative-capacitance FinFET without hysteresis

Abstract: In this work, we report the first Negative-Capacitance FinFET. ALD Hf042Zr058O2 is added on top of the FinFET's gate stack. The test devices have a floating internal gate that can be electrically probed. Direct measurement found the small-signal voltage amplified by 1.6X maximum at the internal gate in agreement with the improvement of the subthreshold swing (from 87 to 55mV/decade). ION increased by >25% for the IOFF. For the first time, we demonstrate that raising HfZrO2 ferroelectricity, by annealing at higher temperature, reduces and eliminates IV hysteresis and increases the voltage gain. These discoveries will guide future theoretical and experimental work.

Hyperdimensional computing with 3D VRRAM in-memory kernels: Device-architecture co-design for energy-efficient, error-resilient language recognition

Abstract: The ability to learn from few examples, known as one-shot learning, is a hallmark of human cognition. Hyperdimensional (HD) computing is a brain-inspired computational framework capable of one-shot learning, using random binary vectors with high dimensionality. Device-architecture co-design of HD cognitive computing systems using 3D VRRAM/CMOS is presented for language recognition. Multiplication-addition-permutation (MAP), the central operations of HD computing, are experimentally demonstrated on 4-layer 3D VRRAM/FinFET as non-volatile in-memory MAP kernels. Extensive cycle-to-cycle (up to 1012 cycles) and wafer-level device-to-device (256 RRAMs) experiments are performed to validate reproducibility and robustness. For 28-nm node, the 3D in-memory architecture reduces total energy consumption by 52.2% with 412 times less area compared with LP digital design (using registers as memory), owing to the energy-efficient VRRAM MAP kernels and dense connectivity. Meanwhile, the system trained with 21 samples texts achieves 90.4% accuracy recognizing 21 European languages on 21,000 test sentences. Hard-error analysis shows the HD architecture is amazingly resilient to RRAM endurance failures, making the use of various types of RRAMs/CBRAMs (1k ∼ 10M endurance) feasible.

Four-layer 3D vertical RRAM integrated with FinFET as a versatile computing unit for brain-inspired cognitive information processing

Abstract: For the first time, a four-layer HfO x -based 3D vertical RRAM, the “tallest” one ever reported, is developed and integrated with FinFET selector. Uniform memory performance across four layers is obtained (±0.8V switching, 106 endurance, 104s@125°C). SPICE simulations show that high drive current of pillar select transistors is required for high-rise 3D RRAM arrays. The four-layer 3D RRAM is a versatile computing unit for (a) brain-inspired computing and (b) in-memory computing. (a) Stochastic RRAM synapses enable robust pattern learning for a 3D neuromorphic visual system. The 3D architecture with dense and balanced neuron-synapse connections provides 55% EDP savings and 74% V DD reduction (enhanced robustness) compared with conventional 2D architecture; (b) in-memory logic such as NAND, NOR, and bit shift, are essential elements for hyper-dimensional computing. Utilizing the unique vertical connection of 3D RRAM cells, these operations are performed with little data movement.

Substrate noise-coupling characterization and efficient suppression in CMOS technology

Abstract: This brief investigates the substrate noise coupling using S-parameters measurement. Radio frequency domain analysis shows that the noise isolation is strongly dependent on layout geometry, including the parameters such as p-n junction, physical separation distance, guard ring (GR), and deep n-well (DNW). We found that the noise coupling can be efficiently diminished by incorporating GR and DNW structures.

Atomic-Monolayer Two-Dimensional Lateral Quasi-Heterojunction Bipolar Transistors with Resonant Tunneling Phenomenon

Abstract: High-frequency operation with ultrathin, lightweight, and extremely flexible semiconducting electronics is highly desirable for the development of mobile devices, wearable electronic systems, and defense technologies. In this work, the experimental observation of quasi-heterojunction bipolar transistors utilizing a monolayer of the lateral WSe2–MoS2 junctions as the conducting p–n channel is demonstrated. Both lateral n–p–n and p–n–p heterojunction bipolar transistors are fabricated to exhibit the output characteristics and current gain. A maximum common-emitter current gain of around 3 is obtained in our prototype two-dimensional quasi-heterojunction bipolar transistors. Interestingly, we also observe the negative differential resistance in the electrical characteristics. A potential mechanism is that the negative differential resistance is induced by resonant tunneling phenomenon due to the formation of quantum well under applying high bias voltages. Our results open the door to two-dimensional material...

Leakage current mechanisms and leakage reduction techniques in deep-submicrometer CMOS circuits

Abstract: High leakage current in deep-submicrometer regimes is becoming a significant contributor to power dissipation of CMOS circuits as threshold voltage, channel length, and gate oxide thickness are reduced. Consequently, the identification and modeling of different leakage components is very important for estimation and reduction of leakage power, especially for low-power applications. This paper reviews various transistor intrinsic leakage mechanisms, including weak inversion, drain-induced barrier lowering, gate-induced drain leakage, and gate oxide tunneling. Channel engineering techniques including retrograde well and halo doping are explained as means to manage short-channel effects for continuous scaling of CMOS devices. Finally, the paper explores different circuit techniques to reduce the leakage power consumption.

The future of wires

Abstract: Concern about the performance of wires wires in scaled technologies has led to research exploring other communication methods. This paper examines wire and gate delays as technologies migrate from 0.18-/spl mu/m to 0.035-/spl mu/m feature sizes to better understand the magnitude of the the wiring problem. Wires that shorten in length as technologies scale have delays that either track gate delays or grow slowly relative to gate delays. This result is good news since these "local" wires dominate chip wiring. Despite this scaling of local wire performance, computer-aided design (CAD) tools must still become move sophisticated in dealing with these wires. Under scaling, the total number of wires grows exponentially, so CAD tools will need to handle an ever-growing percentage of all the wires in order to keep designer workloads constant. Global wires present a more serious problem to designers. These are wires that do not scale in length since they communicate signals across the chip. The delay of these wives will remain constant if repeaters are used meaning that relative to gate delays, their delays scale upwards. These increased delays for global communication will drive architectures toward modular designs with explicit global latency mechanisms.

The future of electronics based on memristive systems

Abstract: A memristor is a resistive device with an inherent memory. The theoretical concept of a memristor was connected to physically measured devices in 2008 and since then there has been rapid progress in the development of such devices, leading to a series of recent demonstrations of memristor-based neuromorphic hardware systems. Here, we evaluate the state of the art in memristor-based electronics and explore where the future of the field lies. We highlight three areas of potential technological impact: on-chip memory and storage, biologically inspired computing and general-purpose in-memory computing. We analyse the challenges, and possible solutions, associated with scaling the systems up for practical applications, and consider the benefits of scaling the devices down in terms of geometry and also in terms of obtaining fundamental control of the atomic-level dynamics. Finally, we discuss the ways we believe biology will continue to provide guiding principles for device innovation and system optimization in the field. This Perspective evaluates the state of the art in memristor-based electronics and explores the future development of such devices in on-chip memory, biologically inspired computing and general-purpose in-memory computing.

Graphene and two-dimensional materials for silicon technology.

Abstract: The development of silicon semiconductor technology has produced breakthroughs in electronics—from the microprocessor in the late 1960s to early 1970s, to automation, computers and smartphones—by downscaling the physical size of devices and wires to the nanometre regime. Now, graphene and related two-dimensional (2D) materials offer prospects of unprecedented advances in device performance at the atomic limit, and a synergistic combination of 2D materials with silicon chips promises a heterogeneous platform to deliver massively enhanced potential based on silicon technology. Integration is achieved via three-dimensional monolithic construction of multifunctional high-rise 2D silicon chips, enabling enhanced performance by exploiting the vertical direction and the functional diversification of the silicon platform for applications in opto-electronics and sensing. Here we review the opportunities, progress and challenges of integrating atomically thin materials with silicon-based nanosystems, and also consider the prospects for computational and non-computational applications. Progress in integrating atomically thin two-dimensional materials with silicon-based technology is reviewed, together with the associated opportunities and challenges, and a roadmap for future applications is presented.

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

Abstract: Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed