151 citations · 204 across the 13 of their papers we have counts for
14 papers
A FeFET Voltage-to-Time Converter with Offset Trim by Programmed Multilevel State in 28-nm CMOS
Jeries Mattar, Hanaa Eqeiq, Stefan Dünkel +4
The offset and gain of voltage-to-time converters (VTCs) in time-domain circuits vary with process, supply, and temperature. This work presents a VTC whose nonvolatile input-referr…
A 185 TOPS/W/mm2 Bayesian Inference Engine with 640 aJ Write-Free FeFET GRNG for Uncertainty-Aware Aerial Search and Rescue
Zephan M. Enciso, Xuezhong Niu, Xingtian Wang +9
Aerial search and rescue missions require fast and reliable victim detection under uncertain and rapidly changing environments. Deterministic deep learning models can produce overc…
Probabilistic Tree Inference Enabled by FDSOI Ferroelectric FETs
Pengyu Ren, Xingtian Wang, Boyang Cheng +9
Artificial intelligence applications in autonomous driving, medical diagnostics, and financial systems increasingly demand machine learning models that can provide robust uncertain…
Symbol Detection in a MIMO Wireless Communication System Using a FeFET-coupled CMOS Ring Oscillator Array
Harsh Kumar Jadia, Abhinaba Ghosh, Md Hanif Ali +11
Symbol decoding in multiple-input multiple-output (MIMO) wireless communication systems requires the deployment of fast, energy-efficient computing hardware deployable at the edge.…
Cryogenic Characterization of Ferroelectric Non-volatile Capacitors
Madhav Vadlamani, Dyutimoy Chakraborty, Jianwei Jia +5
Ferroelectric-based capacitive crossbar arrays have been proposed for energy-efficient in-memory computing in the charge domain. They combat the challenges like sneak paths and hig…
A Bio-inspired Asymmetric Double-Gate Ferroelectric FET for Emulating Astrocyte and Dendrite Dynamics in Neuromorphic Systems
Zhouhang Jiang, A N M Nafiul Islam, Zhuangyu Han +13
Neuromorphic systems seek to replicate the functionalities of biological neural networks to attain significant improvements in performance and efficiency of AI computing platforms.…
A Reconfigurable Time-Domain In-Memory Computing Macro using FeFET-Based CAM with Multilevel Delay Calibration in 28 nm CMOS
Jeries Mattar, Mor M. Dahan, Stefan Dunkel +5
Time-domain nonvolatile in-memory computing (TD-nvIMC) offers a promising pathway to reduce data movement and improve energy efficiency by encoding computation in delay rather than…
Paving the Way for Pass Disturb Free Vertical NAND Storage via A Dedicated and String-Compatible Pass Gate
Zijian Zhao, Sola Woo, Khandker Akif Aabrar +22
In this work, we propose a dual-port cell design to address the pass disturb in vertical NAND storage, which can pass signals through a dedicated and string-compatible pass gate. W…
Ferroelectric MirrorBit-Integrated Field-Programmable Memory Array for TCAM, Storage, and In-Memory Computing Applications
Paritosh Meihar, Rowtu Srinu, Sandip Lashkare +6
In-memory computing on a reconfigurable architecture is the emerging field which performs an application-based resource allocation for computational efficiency and energy optimizat…
Powering Disturb-Free Reconfigurable Computing and Tunable Analog Electronics with Dual-Port Ferroelectric FET
Zijian Zhao, Shan Deng, Swetaki Chatterjee +15
Single-port ferroelectric FET (FeFET) that performs write and read operations on the same electrical gate prevents its wide application in tunable analog electronics and suffers fr…
FeFET-based MirrorBit cell for High-density NVM storage
Paritosh Meihar, Rowtu Srinu, Vivek Saraswat +6
HfO2-based Ferroelectric field-effect transistor (FeFET) has become a center of attraction for non-volatile memory applications because of their low power, fast switching speed, hi…
Ferroelectric FET based Context-Switching FPGA Enabling Dynamic Reconfiguration for Adaptive Deep Learning Machines
Yixin Xu, Zijian Zhao, Yi Xiao +16
Field Programmable Gate Array (FPGA) is widely used in acceleration of deep learning applications because of its reconfigurability, flexibility, and fast time-to-market. However, c…
Hafnia-based Double Layer Ferroelectric Tunnel Junctions as Artificial Synapses for Neuromorphic Computing
Benjamin Max, Michael Hoffmann, Halid Mulaosmanovic +2
Ferroelectric tunnel junctions (FTJ) based on hafnium zirconium oxide (Hf1-xZrxO2; HZO) are a promising candidate for future applications, such as low-power memories and neuromorph…
A computational study of hafnia-based ferroelectric memories: from ab initio via physical modeling to circuit models of ferroelectric device
Milan Pešić, Christopher Künneth, Michael Hoffmann +7
The discovery of ferroelectric properties of binary oxides revitalized the interest in ferroelectrics and bridged the scaling gap between the state-of-the-art semiconductor technol…