Investigation of Variability in Device Design on Saturation Characteristics of Nanowire Tunnel FETs

Silicon ◽  
2021 ◽  
Author(s):  
Abhishek Acharya
Keyword(s):  
2020 ◽  
Vol 59 (SG) ◽  
pp. SGGA06
Author(s):  
Koichi Fukuda ◽  
Naoya Nogami ◽  
Shogo Kunisada ◽  
Yasuyuki Miyamoto

2021 ◽  
Author(s):  
Abhishek Acharya

Abstract Estimation of the saturation voltages of beyond CMOS devices is essential for the accurate circuit design and analysis. In this work, we look at the influence of device design parameters on the saturation voltage (VDSAT) of a Tunnel Field Effect Transistor (TFET) using 3D TCAD Numerical Simulations. The variation in channel length, underlap at gate-drain, source/drain doping, and the source/channel material are some of the vital optimization parameters in the design and optimization of TFET based circuits. We observe, with the increasing value of drain bias (VDS), TFET device initially enters in the soft saturation state and subsequently a deep saturation state is attained. These voltages are altered with device variability and hence the analog performance. An increase in drain (source) doping increases (decreases) the soft saturation voltage of TFETs. It is also found that an early onset of saturation can be achieved by the gate-drain underlap in TFETs. The impact of short channel lengths is to worsen the perfect saturation phenomenon in Tunnel FETs. In addition, the reduction in nanowire diameter delays the saturation by few milivolts.


2019 ◽  
Author(s):  
K. Fukuda ◽  
N. Nogami ◽  
S. Kunisada ◽  
Y. Miyamoto

Author(s):  
M. Peirlinck ◽  
F. Sahli Costabal ◽  
J. Yao ◽  
J. M. Guccione ◽  
S. Tripathy ◽  
...  

AbstractPrecision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.


2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.


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