( Invited ) Cryogenic Electrochemical Ion Transport and Switching Dynamics In Solid-State Electronic Synapses
The rapid advancement of artificial intelligence (AI) has driven significant progress in data processing, pattern recognition, and autonomous decision-making across various domains, including healthcare, smart manufacturing, and scientific computing. These advances increasingly rely on large-scale artificial neural networks, whose expanding model sizes demand substantial computational and memory resources. However, conventional von Neumann architectures, which physically separate processing and memory, suffer from intrinsic limitations including severe data-transfer bottlenecks. As a result, global data-center electricity consumption is projected to reach ~945 TWh by 2030, underscoring the urgent need for energy-efficient computing paradigms. Although software-level approaches such as model compression, quantization, and pruning can partially reduce computational cost, they cannot fundamentally overcome the hardware inefficiencies of von Neumann systems. This challenge has motivated the development of neuromorphic computing based on non–von Neumann architectures, which co-locate memory and computation to emulate brain-inspired information processing. In particular, cryogenic neuromorphic computing has emerged as a promising direction, especially when integrated with quantum computing (QC). Cryogenic operation offers reduced thermal noise and power dissipation, enabling high performance and ultra-low-power computation. However, such systems require electronic synapses that can be placed close to quantum processors, reliably store quantum-generated information, and operate efficiently at cryogenic temperatures. Spiking neural networks (SNNs) offer a biologically inspired framework well-suited for this purpose. In SNNs, neurons integrate inputs and emit spikes upon reaching a threshold, while synapses adapt their weights through spike-timing-dependent plasticity (STDP). This event-driven, time-domain encoding enables highly energy-efficient computation and naturally unifies memory and processing. In contrast, conventional memories such as static random access memory (SRAM), dynamic random access memory (DRAM), and NOR/NAND Flash are optimized for digital storage rather than analog synaptic weight modulation. Their discrete states, high power consumption, limited endurance, and strict logic-memory separation significantly hinder their suitability for neuromorphic systems, particularly under cryogenic operation. Emerging memory technologies capable of continuous conductance modulation and low-energy operation are therefore essential for electronic synapses. Among them, resistive RAM (RRAM), electrochemical RAM (ECRAM), and conductive-bridge RAM (CBRAM) have garnered significant attention due to their simple structures, scalability, and low switching energy. ECRAM relies on ionic drift mechanisms analogous to biological synapses, while CBRAM modulates resistance via the formation and rupture of metallic filaments, enabling analog conductance control. In this work, we investigate cryogenic electronic synapses based on LiCoO 2 (LCO) and Ag/WTe 2 CBRAM structures over a wide temperature range from cryogenic to room temperature. For the LCO synapse, an Al-rich LiCoO 2 (LACO) Li-ion nano-reservoir stabilizes Li ions, reduces interfacial electric fields, and suppresses leakage current. At cryogenic temperatures, suppressed ion and electron diffusion enhances field-driven processes, resulting in significantly improved synaptic performance, including an expanded memory window (~1.6 to ~423), improved linearity, and enhanced STDP. We further demonstrate an Ag/WTe 2 CBRAM synaptic device featuring forming-free switching, self-compliance, and an ultra-low operating voltage of ~0.2 V. The WTe 2 matrix enables stable Ag filament formation, supporting key synaptic functions such as short-term plasticity, long-term potentiation, and STDP. I-V measurements from 4 K to 300 K reveal suppressed memory behavior below 10 K due to quenched thermally activated Ag + migration. In contrast, memory windows and resistance oscillations emerge at 20–30 K, which are likely attributed to electron hopping during the early stages of filament nucleation. These results decouple thermal and field-driven effects, providing critical guidance for designing reliable cryogenic electronic synapses for future neuromorphic-quantum computing systems.