Phase-change heterostructure enables ultralow noise and drift for memory operation.
Keyuan DingJiang-Jing WangYuxing ZhouHe TianLu LuRiccardo MazzarelloChunlin JiaWei ZhangFeng RaoEvan MaPublished in: Science (New York, N.Y.) (2019)
Artificial intelligence and other data-intensive applications have escalated the demand for data storage and processing. New computing devices, such as phase-change random access memory (PCRAM)-based neuro-inspired devices, are promising options for breaking the von Neumann barrier by unifying storage with computing in memory cells. However, current PCRAM devices have considerable noise and drift in electrical resistance that erodes the precision and consistency of these devices. We designed a phase-change heterostructure (PCH) that consists of alternately stacked phase-change and confinement nanolayers to suppress the noise and drift, allowing reliable iterative RESET and cumulative SET operations for high-performance neuro-inspired computing. Our PCH architecture is amenable to industrial production as an intrinsic materials solution, without complex manufacturing procedure or much increased fabrication cost.
Keyphrases
- artificial intelligence
- big data
- air pollution
- working memory
- machine learning
- induced apoptosis
- electronic health record
- deep learning
- minimally invasive
- magnetic resonance imaging
- oxidative stress
- cell cycle arrest
- computed tomography
- heavy metals
- risk assessment
- magnetic resonance
- data analysis
- cell proliferation
- dual energy