A FerroFET-Based In-Memory Processor for Solving Distributed and Iterative Optimizations via Least-Squares Method

Yoon, Insik; Khan, Asif Islam; Datta, Suman; Raychowdhury, Arijit; Chang, Muya; Ni, Kai; Jerry, Matthew; Gangopadhyay, Samantak; Smith, Gus; Hamam, Tomer; Romberg, Justin; Narayanan, Vijaykrishnan

doi:10.1109/jxcdc.2019.2930222

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…The second approach, i.e. performing a logic operation within one FeFET, was pursued for simple logic gates [192], for ternary content addressable memory [198,199] where one cell utilizes XOR logic, for half and full adder circuits [200] as well as for vector-matrix-multipliers (VMM) [201,202]. The FeFET-based VMMs are a basic element of, for example, deep neural networks [201], and have been additionally exploited for optimizations via the least-squares minimization [202].…”

Section: In-memory Computingmentioning

confidence: 99%

“…performing a logic operation within one FeFET, was pursued for simple logic gates [192], for ternary content addressable memory [198,199] where one cell utilizes XOR logic, for half and full adder circuits [200] as well as for vector-matrix-multipliers (VMM) [201,202]. The FeFET-based VMMs are a basic element of, for example, deep neural networks [201], and have been additionally exploited for optimizations via the least-squares minimization [202]. In general, computations within the memory array are very flexible in terms of possible logic operations because the logic functionality is determined by the number of utilized FeFETs and their stored states, not by the array periphery.…”

Section: In-memory Computingmentioning

confidence: 99%

See 1 more Smart Citation

Ferroelectric field-effect transistors based on HfO₂: a review

Mulaosmanovic¹,

Breyer²,

Dünkel³

et al. 2021

Nanotechnology

208

116

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In this article, we review the recent progress of ferroelectric field-effect transistors (FeFETs) based on ferroelectric hafnium oxide (HfO2), ten years after the first report on such a device. With a focus on the use of FeFET for nonvolatile memory application, we discuss its basic operation principles, switching mechanisms, device types, material properties and array structures. Key device performance metrics such as cycling endurance, retention, memory window, multi-level operation and scaling capability are analyzed. We also briefly survey recent developments in alternative applications for FeFETs including neuromorphic and in-memory computing as well as radiofrequency devices.

show abstract

Section: In-memory Computingmentioning

confidence: 99%

Section: In-memory Computingmentioning

confidence: 99%

Ferroelectric field-effect transistors based on HfO₂: a review

Mulaosmanovic¹,

Breyer²,

Dünkel³

et al. 2021

Nanotechnology

208

116

View full text Add to dashboard Cite

show abstract

“…Although ferroelectric-transistor-based NNs are expected to show high energy efficiency and several NN array structures exhibit promising characteristics, several specific issues should be investigated from the array perspective. [172][173][174][175] First, the high capacitance between word lines can increase the energy consumption. Additionally, the interference and disturbance characteristics can cause inaccurate NN training and thus should be rigorously investigated.…”

Section: Ferroelectric Transistor Arrays For Nnsmentioning

confidence: 99%

Ferroelectric Transistors for Memory and Neuromorphic Device Applications

Kim

Lee

2023

Advanced Materials

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Because a non-centrosymmetric crystal structure is mandatory for ferroelectric properties, very few materials are classified as ferroelectric. Historically, materials with complex crystal structures, such as BaTiO 3 (BTO), Pb[Zr x Ti 1−x ]O 3 (PZT), and Sr 2 Bi 2 TaO 9 (SBT), have been found to exhibit ferroelectricity. [5][6][7][8][9] Due to their spontaneous polarization, ferroelectric materials are diversely applied in memory devices, actuators, sensors, and energy storage. [1,2,[10][11][12][13] As the polarization state of ferroelectric materials can be regulated by an external electric field and the polarization is retained after the removal of the external electric field, such materials have the potential for applications involving high-performance non volatile memories. [9,14] However, some issues of conventional ferroelectric materials, such as high annealing temperatures, the requirement of noble electrode materials, and the diffusion of elements such as Pb complicate the integration of the previously reported ferroelectric materials into complementary metal-oxide semiconductors (CMOSs). [15][16][17][18] Furthermore, the large dielectric constants of perovskite-based ferroelectrics lead to high depolarization fields; [9,16] thus, the materials have short retention time and exhibit unstable polarization. [9] Additionally, the limited thickness scalability, complex etching process, and fatigue behaviors of perovskite-based ferroelectrics hinder the development of highly scaled ferroelectric devices. [9,18] Thus, semiconductor devices based on ferroelectric materials have limited commercial applications. Since the development of hafnia-based ferroelectrics, ferroelectric hafnia has attracted immense attention toward the development of semiconductor devices. [19][20][21] Compared to perovskite-based ferroelectric materials, hafnia-based ferroelectrics exhibit high scalability, and have high coercive electric fields (E c ) and low dielectric permittivity values, making them suitable for the fabrication of high-performance memory devices. [2,16,20,22] Additionally, hafnia is compatible with Si-based CMOS processes. Due to these advantages, hafnia-based ferroelectrics have been used in next-generation memory devices (such as ferroelectric capacitors, transistors, and tunnel junctions [FTJs]). [22][23][24][25][26] This paper reviews the recent findings and applications of hafnia-based ferroelectrics, highlighting the recent advances in ferroelectric transistors for next-generation memory and neuromorphic Ferroelectric materials have been intensively investigated for highperformance nonvolatile memory devices in the past decades, owing to their nonvolatile polarization characteristics. Ferroelectric memory devices are expected to exhibit lower power consumption and higher speed than conventional memory devices. However, non-complementary metal-oxidesemiconductor (CMOS) compatibility and degradation due to fatigue of traditional perovskite-based ferroelectric materials have hindered the development of high-density...

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“…This design concept was further applied to RRAM [10,11] and PCM [12] based on redesigned writing and peripheral circuit blocks. Significant potential for using the IMC approach for emerging device-based NVMs was demonstrated by processing the in-FeFET cells with an intrinsic compact area and distinguishable multiple states [13,14]. As a demonstration of PCM, dual-mode double-density PCM based on a novel stressing-mode storage scheme was studied in [15].…”

Section: Bit-cell Level Attemptmentioning

confidence: 99%

A survey of in-spin transfer torque MRAM computing

Cai

Liu

Chen

et al. 2021

Sci. China Inf. Sci.

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In traditional von Neumann computing architectures, the essential transfer of data between the processor and memory hierarchies limits the computational efficiency of next-generation system-on-a-chip. The emerging in-memory computing (IMC) approach addresses this issue and facilitates the movement of significant data and rapid computations. Among the different memory types, intrinsic energy efficiency is demonstrated by in-magnetic random access memory (MRAM) computing with a low-power spintronic magnetic tunnel junction device and hybrid integration at an advanced complementary metal-oxide semiconductor node. This study reviews state-of-the-art techniques for managing IMC with an emphasis on spin-transfer torque-MRAM computing via design schemes at the bit-cell, circuit, and system levels. In addition, this study presents effective design techniques and potential challenges and demonstrates the existing limitations of in-MRAM computing and potential methods for overcoming these issues. This study also considers the design technology co-optimization from the IMC perspective.

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A FerroFET-Based In-Memory Processor for Solving Distributed and Iterative Optimizations via Least-Squares Method

Cited by 7 publications

References 33 publications

Ferroelectric field-effect transistors based on HfO₂: a review

Ferroelectric field-effect transistors based on HfO₂: a review

Ferroelectric Transistors for Memory and Neuromorphic Device Applications

A survey of in-spin transfer torque MRAM computing

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A FerroFET-Based In-Memory Processor for Solving Distributed and Iterative Optimizations via Least-Squares Method

Cited by 7 publications

References 33 publications

Ferroelectric field-effect transistors based on HfO2: a review

Ferroelectric field-effect transistors based on HfO2: a review

Ferroelectric Transistors for Memory and Neuromorphic Device Applications

A survey of in-spin transfer torque MRAM computing

Contact Info

Product

Resources

About

Ferroelectric field-effect transistors based on HfO₂: a review

Ferroelectric field-effect transistors based on HfO₂: a review