Review summarizing innovations in phase-change memory energy efficiency via material scaling and thermal confinement, with discussion of attojoule-scale theoretical limits constrained by parasitics.
J Vac Sci Technol B
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Neuromorphic computing using compute-in-memory, analog dynamics, and sparse brain-inspired communication offers a route to more energy-efficient AI beyond traditional CMOS scaling limits.
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Energy and Scaling Limits of Phase-Change Memory
Review summarizing innovations in phase-change memory energy efficiency via material scaling and thermal confinement, with discussion of attojoule-scale theoretical limits constrained by parasitics.
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Neuromorphic Computing for Low-Power Artificial Intelligence
Neuromorphic computing using compute-in-memory, analog dynamics, and sparse brain-inspired communication offers a route to more energy-efficient AI beyond traditional CMOS scaling limits.