Publication date: 24th July 2026
Neuromorphic computing has been widely explored to overcome the energy and latency limits of von Neumann architectures. Memristor-based crossbar arrays enable in-memory computing, but their deployment remains limited by stochastic switching, device variability, and poor stability of analog weights. Here, we explore an alternative hardware direction that preserves parallel analog computation while eliminating device-level stochasticity through deterministic weight representations. This enables reliable and scalable inference without relying on fragile analog state control. We emphasize that materials and integration, rather than individual devices, define ultimate performance limits. In this context, three-dimensional integration (M3D) provides a critical pathway by vertically coupling sensing, memory, and computing layers, minimizing data movement and enabling parallel processing. Our prior demonstrations of stackable neuromorphic systems and monolithic 3D integration highlight improvements in bandwidth, latency, and energy efficiency. Finally, we extend this framework toward ultralow-energy operation using strain-engineered devices, where dynamic material modulation enables attojoule-scale switching. This combined strategy—deterministic weight architectures, M3D integration, and new switching mechanisms—offers a scalable path toward next-generation unconventional computing approaching fundamental energy limit.
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