Realistic fault modelling to enable optimization of low power IoT and Cognitive fault-tolerant computing systems
Lead Research Organisation:
Liverpool John Moores University
Department Name: School of Engineering
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
Brown J
(2023)
A Pragmatic Model to Predict Future Device Aging
in IEEE Access
Gao R
(2021)
A Comparative Study of AC Positive Bias Temperature Instability of Germanium nMOSFETs With GeO2/Ge and Si-cap/Ge Gate Stack
in IEEE Journal of the Electron Devices Society
Liu C
(2022)
Realization of NOR logic using Cu/ZnO/Pt CBRAM
Liu C
(2021)
Investigation on the Implementation of Stateful Minority Logic for Future In-Memory Computing
in IEEE Access
Liu X
(2023)
Equiprobability-Based Local Response Surface Method for High-Sigma Yield Estimation With Both High Accuracy and Efficiency
in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Mehedi M
(2021)
On the Accuracy in Modeling the Statistical Distribution of Random Telegraph Noise Amplitude
in IEEE Access
Tok K
(2022)
AC RTN: Testing, Modeling, and Prediction
in IEEE Transactions on Electron Devices
| Description | An novel integral methodology has been developed for modelling the random telegraph noises in nanoscale devices. This is the first model that is capable to predict the RTN at long time windows. When compared with the early models, the advantages of this model include (i) It does not require selecting devices, (ii) It can model the fluctuation in both directions, (iii) It does not require separating the measured results into the contribution of individual devices experimentally. (iv) It reduces computing time by evaluating the probability directly and does not require the Monte Carlo simuation. |
| Exploitation Route | The integral model has been published for potential users to simulate and predict the RTN in their devices. The team at Liverpool John Moores University is collaborating with Crypto-Quantique to model the RTN in their products. |
| Sectors | Digital/Communication/Information Technologies (including Software),Electronics,Security and Diplomacy |
| Description | The integral model developed in this project is being used to model the Random Telegraph Noise in the commercial products of Crypto-Quantique to improve the performance of the product. |
| First Year Of Impact | 2023 |
| Sector | Digital/Communication/Information Technologies (including Software),Electronics,Security and Diplomacy |
| Impact Types | Economic |
| Description | Collaboration with CQ |
| Organisation | Crypto Quantique |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | The Random Telegraph Noise model developed in this project is applied to the RTN in the product of Crypto Quantique. |
| Collaborator Contribution | The partner designed and supplied the test samples. |
| Impact | This collaboration started in January 2023 and there is no outputs/outcome yet. |
| Start Year | 2023 |