A 4T2R RRAM Bit Cell for Highly Parallel Ternary Content Addressable Memory

Wang, Xuehong; Wang, Linfang; Ye, Wang; An, Junjie; Dou, Chunmeng; Wu, Zuheng; Zhang, Xumeng; Liu, Jing; Zhang, Chenggao; Yao, Zhihong; Yu, Zhibin; Shi, Tao; Chen, Chixiao; Jiang, Xiping; Chang, Meng-Fan; Liu, Qi

doi:10.1109/ted.2021.3107497

Cited by 18 publications

(5 citation statements)

References 24 publications

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“…The calculation of Hamming distance (HD) is given by an XOR‐accumulation operation expressed by Equation (4):

HD ((), boldQ, boldP) goodbreak= \sum_{i = 1}^{m} Q_{i} \oplus P_{i}

where Q i / P i denotes the i th bit of the m ‐bit code Q / P . TCAMs based on nonvolatile devices have shown a powerful performance in memory‐centric applications such as pattern matching, 17,18 data query, 19 tree‐based machine learning, 20,21 and the memory augmented neural network 22 owing to its ultra‐high parallelism to perform the Hamming distance‐based search.…”

Section: Resultsmentioning

confidence: 99%

“…where Q i /P i denotes the i th bit of the m-bit code Q/P. TCAMs based on nonvolatile devices have shown a powerful performance in memory-centric applications such as pattern matching, 17,18 data query, 19 tree-based machine learning, 20,21 and the memory augmented neural network 22 owing to its ultra-high parallelism to perform the Hamming distance-based search. Figure 4A schematically illustrates the structure of the proposed 2SSM TCAM and the corresponding definition of the states.…”

Section: Ssm Tcam For Hamming Distance Computingmentioning

confidence: 99%

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Self‐selective memristor‐enabled in‐memory search for highly efficient data mining

Ling

Huang

et al. 2023

InfoMat

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Similarity search, that is, finding similar items in massive data, is a fundamental computing problem in many fields such as data mining and information retrieval. However, for large‐scale and high‐dimension data, it suffers from high computational complexity, requiring tremendous computation resources. Here, based on the low‐power self‐selective memristors, for the first time, we propose an in‐memory search (IMS) system with two innovative designs. First, by exploiting the natural distribution law of the devices resistance, a hardware locality sensitive hashing encoder has been designed to transform the real‐valued vectors into more efficient binary codes. Second, a compact memristive ternary content addressable memory is developed to calculate the Hamming distances between the binary codes in parallel. Our IMS system demonstrated a 168× energy efficiency improvement over all‐transistors counterparts in clustering and classification tasks, while achieving a software‐comparable accuracy, thus providing a low‐complexity and low‐power solution for in‐memory data mining applications.

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“…The calculation of Hamming distance (HD) is given by an XOR‐accumulation operation expressed by Equation (4):

HD ((), boldQ, boldP) goodbreak= \sum_{i = 1}^{m} Q_{i} \oplus P_{i}

Section: Resultsmentioning

confidence: 99%

Section: Ssm Tcam For Hamming Distance Computingmentioning

confidence: 99%

Self‐selective memristor‐enabled in‐memory search for highly efficient data mining

Ling

Huang

et al. 2023

InfoMat

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“…Recently, highperformance TCAMs based on emergent memory technologies, such as, phase-change memory, resistive switching memory, and ferroelectric transistors, have been proposed. [126,127,[130][131][132] Among the diverse memory types, ferroelectric transistors are promising candidates owing to their CMOS compatibility, low leakage current, and high operation speed. [133][134][135] Ferroelectric TCAMs have been fabricated by integrating two ferroelectric transistors (Figure 8a,b) [134] with the same match-line; each gate electrode is connected to separate search-lines (Figure 8c).…”

Section: Ternary Content Addressable Memoriesmentioning

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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“…Notably, memristor crossbar circuit offers the principal advantage of inherent vector-by-matrix multiplication (VMM) operation, which enables physical-level in-memory computing using Ohm's and Kirchhoff 's current laws and can be applied for various in-memory computing applications such as neuromorphic engineering, [1][2][3][4][5][6][7][8][9] physical unclonable function, [10][11][12][13][14][15][16] and content-addressable memory (CAM). [17][18][19][20][21][22][23][24][25][26][27] A CAM is a type of memory that receives an input vector, compares it with all stored vectors, and returns a related vector output. Generally, CAM is classified into two types: binary CAM (BCAM) having two states of "0" or "1", and ternary CAM (TCAM) having three states with the addition of "X" state (i.e., "do not care").…”

Section: Introductionmentioning

confidence: 99%

“…Typically, memristorbased CAMs are configured as 2T2R or nT2R with selector devices or additional gate-connected transistors to improve read margin. [17][18][19][20][21][22][23][24] It is believed that a passive memristor crossbar circuit can also be utilized for CAM applications if the intrinsic nonideal problems of passive crossbar including program errors, line resistance, and sneak current can be sufficiently suppressed. However, few studies on CAM technology with passive crossbar array have been investigated although passive memristor crossbar circuit is considered advantageous for high-density integration compared with active array based on NOR flash array architecture.…”

Section: Introductionmentioning

confidence: 99%

Memristor Crossbar Circuit for Ternary Content‐Addressable Memory with Fine‐Tuning Operation

Youn

Kim

et al. 2023

Advanced Intelligent Systems

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Memristor‐based ternary content‐addressable memory (TCAM) has emerged as an alternative to conventional static random‐access memory (SRAM)‐based TCAM because of its high‐density integration and zero‐static energy consumption. Herein, 0T2R TCAM operation on a 32 × 32 passive memristor crossbar circuit is experimentally verified. The effective margin, which is the difference between the match case and 1‐bit mismatch case, is improved through precise tuning operations. Moreover, the number of mismatch bits and match cases can be accurately detected thanks to the linear relationship between the number of mismatch bits and match‐line current. In addition, the nonideal effects in the passive crossbar array including dynamic voltage drop and sneak current are analyzed through SPICE studies. These results indicate that the proposed TCAM operating conditions can ensure stable TCAM operation in larger arrays.

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A 4T2R RRAM Bit Cell for Highly Parallel Ternary Content Addressable Memory

Cited by 18 publications

References 24 publications

Self‐selective memristor‐enabled in‐memory search for highly efficient data mining

Self‐selective memristor‐enabled in‐memory search for highly efficient data mining

Ferroelectric Transistors for Memory and Neuromorphic Device Applications

Memristor Crossbar Circuit for Ternary Content‐Addressable Memory with Fine‐Tuning Operation

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