Injection of Space Radiation Errors on Low-power NVM using Generative Deep Learning
We develop an error model for a Non-Volatile Memory (NVM) device that represents the effects of physical faults experienced in a CubeSat mission in Low Earth Orbit (LEO). The CubeSat carries an off-the-shelf low-power Ferroelectric RAM (FRAM) chip and runs repeated read/write tests according to a predefined pattern. We compare every memory snapshot received from the satellite with a reference bitmap to identify bit errors. We visually analyze erroneous values distributions in the memory snapshots and we understand that they are frequently distributed according to regular spatial patterns. We define a generative deep learning technique to automatically learn these spatial patterns and to s...
| Authors | Eleonora Giudici, Antonio Miele, Luca Mottola |
|---|---|
| Primary category | cs.AR |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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