Universität Stuttgart

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Institute: search.filters.UBSInstitut.Fakultätsübergreifend / Sonstige EinrichtungAuthor: search.filters.author.Amrouch, Hussam

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Now showing 1 - 6 of 6
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    Nontraditional design of dynamic logics using FDSOI for ultra-efficient computing
    (2023) Kumar, Shubham; Chatterjee, Swetaki; Dabhi, Chetan Kumar; Chauhan, Yogesh Singh; Amrouch, Hussam
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    Modeling and investigating total ionizing dose impact on FeFET
    (2023) Sayed, Munazza; Ni, Kai; Amrouch, Hussam
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    Thermal effects on monolithic 3D ferroelectric transistors for deep neural networks performance
    (2024) Kumar, Shubham; Chauhan, Yogesh Singh; Amrouch, Hussam
    Monolithic three‐dimensional (M3D) integration advances integrated circuits by enhancing density and energy efficiency. Ferroelectric thin‐film transistors (Fe‐TFTs) attract attention for neuromorphic computing and back‐end‐of‐the‐line (BEOL) compatibility. However, M3D faces challenges like increased runtime temperatures due to limited heat dissipation, impacting system reliability. This work demonstrates the effect of temperature impact on single‐gate (SG) Fe‐TFT reliability. SG Fe‐TFTs have limitations such as read‐disturbance and small memory windows, constraining their use. To mitigate these, dual‐gate (DG) Fe‐TFTs are modeled using technology computer‐aided design, comparing their performance. Compute‐in‐memory (CIM) architectures with SG and DG Fe‐TFTs are investigated for deep neural networks (DNN) accelerators, revealing heat's detrimental effect on reliability and inference accuracy. DG Fe‐TFTs exhibit about 4.6x higher throughput than SG Fe‐TFTs. Additionally, thermal effects within the simulated M3D architecture are analyzed, noting reduced DNN accuracy to 81.11% and 67.85% for SG and DG Fe‐TFTs, respectively. Furthermore, various cooling methods and their impact on CIM system temperature are demonstrated, offering insights for efficient thermal management strategies.
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    Cryogenic embedded system to support quantum computing : from 5-nm FinFET to full processor
    (2023) Genssler, Paul R.; Klemme, Florian; Parihar, Shivendra Singh; Brandhofer, Sebastian; Pahwa, Girish; Polian, Ilia; Chauhan, Yogesh Singh; Amrouch, Hussam
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    Cryogenic in-memory computing for quantum processors using commercial 5-nm FinFETs
    (2023) Parihar, Shivendra Singh; Thomann, Simon; Pahwa, Girish; Chauhan, Yogesh Singh; Amrouch, Hussam
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    Printed temperature sensor array for high-resolution thermal mapping
    (2022) Bücher, Tim; Huber, Robert; Eschenbaum, Carsten; Mertens, Adrian; Lemmer, Uli; Amrouch, Hussam
    Fully-printed temperature sensor arrays - based on a flexible substrate and featuring a high spatial-temperature resolution - are immensely advantageous across a host of disciplines. These range from healthcare, quality and environmental monitoring to emerging technologies, such as artificial skins in soft robotics. Other noteworthy applications extend to the fields of power electronics and microelectronics, particularly thermal management for multi-core processor chips. However, the scope of temperature sensors is currently hindered by costly and complex manufacturing processes. Meanwhile, printed versions are rife with challenges pertaining to array size and sensor density. In this paper, we present a passive matrix sensor design consisting of two separate silver electrodes that sandwich one layer of sensing material, composed of poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS). This results in appreciably high sensor densities of 100 sensor pixels per cm 2for spatial-temperature readings, while a small array size is maintained. Thus, a major impediment to the expansive application of these sensors is efficiently resolved. To realize fast and accurate interpretation of the sensor data, a neural network (NN) is trained and employed for temperature predictions. This successfully accounts for potential crosstalk between adjacent sensors. The spatial-temperature resolution is investigated with a specially-printed silver micro-heater structure. Ultimately, a fairly high spatial temperature prediction accuracy of 1.22  °C is attained.