Evaluation of the Sensitivity of RRAM Cells to Optical Fault Injection Attacks

Petryk, Dmytro; Dyka, Zoya; Pérez, Eduardo; Mahadevaiaha, Mamathamba Kalishettyhalli; Kabin, Ievgen; Wenger, Christian; Langendoerfer, Peter

doi:10.1109/dsd51259.2020.00047

Cited by 4 publications

(7 citation statements)

References 9 publications

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“…[206,207] Theoretical studies have shown that Al-doped HfO x -based RSM layers are non-stoichiometric after sputtering, oxidation, and ion implantation. [208,209] However, atomic layer deposition results in enhanced chemistry. Utilizing ab initio calculations, recent studies have investigated the charge state, formation energy, and migration barrier of oxygen vacancies in four high-dielectricconstant oxides, i.e., TiO 2 , Al 2 O 3 , Ta 2 O 3 , and HfO 2 .…”

Section: Materials Selection and Experimental Guidancementioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

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Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized.

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Section: Materials Selection and Experimental Guidancementioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

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“…This effect is based on interaction of silicon with laser light. Details about the internal photoelectric effect can be found in [2], [3].…”

Section: Motivationmentioning

confidence: 99%

“…Hence, all attacks have been performed through the front-side of the tested chips. Additional details about the optical FI setup can be found in [3].…”

Section: Setup For Optical Fi Attacks At Ihpmentioning

confidence: 99%

“…Additionally Additional details about IHP RRAM structures, i.e. switching behavior, purpose of transistor and MIM structure, can be found in [3], [8]. The IHP 4 kbit RRAM chips have metal fillers only in two metal layers, i.e.…”

Section: Attacks Against Ihp Rram Structuresmentioning

confidence: 99%

“…Nevertheless some of the RRAM cells that are covered by metal fillers were also influenced but the success rate of the FI attacks for these cells is significantly lower. A detailed description of the experiments done with IHP RRAM chips and results of the FI attacks performed can be found in [3].…”

Section: Attacks Against Ihp Rram Structuresmentioning

confidence: 99%

See 2 more Smart Citations

Metal Fillers as Potential Low Cost Countermeasure against Optical Fault Injection Attacks

Petryk

Dyka

Katzer

et al. 2020

2020 IEEE East-West Design &Amp; Test Symposium (EWDTS)

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Physically accessible devices such as sensor nodes in Wireless Sensor Networks or "smart" devices in the Internet of Things have to be resistant to a broad spectrum of physical attacks, for example to Side Channel Analysis and to Fault Injection attacks. In this work we concentrate on the vulnerability of ASICs to precise optical Fault Injection attacks. Here we propose to use metal fillers as potential low-cost countermeasure that may be effective against a broad spectrum of physical attacks. In our future work we plan to evaluate different methods of metal fillers placement, to select an effective one and to integrate it as additional design rules into automated design flows.

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Side-Channel Attack with Fault Analysis on Memristor-based Computation-in-Memory

Sapui,

Meschkov,

Tahoori

2024

2024 IEEE 30th International Symposium on on-Line Testing and Robust System Design (IOLTS)

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Evaluation of the Sensitivity of RRAM Cells to Optical Fault Injection Attacks

Cited by 4 publications

References 9 publications

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Metal Fillers as Potential Low Cost Countermeasure against Optical Fault Injection Attacks

Side-Channel Attack with Fault Analysis on Memristor-based Computation-in-Memory

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