scholarly journals OBET: On-the-Fly Byte-Level Error Tracking for Correcting and Detecting Faults in Unreliable DRAM Systems

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8271
Author(s):  
Duy-Thanh Nguyen ◽  
Nhut-Minh Ho ◽  
Weng-Fai Wong ◽  
Ik-Joon Chang

With technology scaling, maintaining the reliability of dynamic random-access memory (DRAM) has become more challenging. Therefore, on-die error correction codes have been introduced to accommodate reliability issues in DDR5. However, the current solution still suffers from high overhead when a large DRAM capacity is used to deliver high performance. We present a DRAM chip architecture that can track faults at byte-level DRAM cell errors to address this problem. DRAM faults are classified as temporary or permanent in our proposed architecture, with no additional pins and with minor DRAM chip modifications. Hence, we achieve reliability comparable to that of other state-of-the-art solutions while incurring negligible performance and energy overhead. Furthermore, the faulty locations are efficiently exposed to the operating system (OS). Thus, we can significantly reduce the required scrubbing cycle by scrubbing only faulty DRAM pages while reducing the system failure probability up to 5000∼7000 times relative to conventional operation.

Nanomaterials ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1401
Author(s):  
Te Jui Yen ◽  
Albert Chin ◽  
Vladimir Gritsenko

Large device variation is a fundamental challenge for resistive random access memory (RRAM) array circuit. Improved device-to-device distributions of set and reset voltages in a SiNx RRAM device is realized via arsenic ion (As+) implantation. Besides, the As+-implanted SiNx RRAM device exhibits much tighter cycle-to-cycle distribution than the nonimplanted device. The As+-implanted SiNx device further exhibits excellent performance, which shows high stability and a large 1.73 × 103 resistance window at 85 °C retention for 104 s, and a large 103 resistance window after 105 cycles of the pulsed endurance test. The current–voltage characteristics of high- and low-resistance states were both analyzed as space-charge-limited conduction mechanism. From the simulated defect distribution in the SiNx layer, a microscopic model was established, and the formation and rupture of defect-conductive paths were proposed for the resistance switching behavior. Therefore, the reason for such high device performance can be attributed to the sufficient defects created by As+ implantation that leads to low forming and operation power.


Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2009
Author(s):  
Fatemeh Najafi ◽  
Masoud Kaveh ◽  
Diego Martín ◽  
Mohammad Reza Mosavi

Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.


Author(s):  
Su-Ting Han ◽  
Jiangming Chen ◽  
Zihao Feng ◽  
Mingtao Luo ◽  
Junjie Wang ◽  
...  

Resistive random access memory (RRAM) based on hybrid organic-inorganic halide perovskite (HOIP) has recently gained significant interests due to its low activation energy of ion migration. HOIP RRAM has been...


2003 ◽  
Author(s):  
Ding-Yeong Wang ◽  
Chao-Hsin Chien ◽  
Ming-Jui Yang ◽  
Peer Lehnen ◽  
Ching-Chich Leu ◽  
...  

2013 ◽  
Vol 8 (1) ◽  
pp. 497 ◽  
Author(s):  
Rui Zhang ◽  
Kuan-Chang Chang ◽  
Ting-Chang Chang ◽  
Tsung-Ming Tsai ◽  
Kai-Huang Chen ◽  
...  

Nanomaterials ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 457 ◽  
Author(s):  
Lei Wu ◽  
Hongxia Liu ◽  
Jinfu Lin ◽  
Shulong Wang

A self-compliance resistive random access memory (RRAM) achieved through thermal annealing of a Pt/HfOx/Ti structure. The electrical characteristic measurements show that the forming voltage of the device annealing at 500 °C decreased, and the switching ratio and uniformity improved. Tests on the device’s cycling endurance and data retention characteristics found that the device had over 1000 erase/write endurance and over 105 s of lifetime (85 °C). The switching mechanisms of the devices before and after annealing were also discussed.


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