CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

CMOS-RRAM 集成为低能耗和高吞吐量的神经拟态计算带来了巨大的希望。然而，大多数依赖于丝状开关的 RRAM 技术都会受到变化和噪声的影响，从而导致计算精度损失、能耗增加以及昂贵的编程和验证方案的开销。我们开发了一种无灯丝、批量开关 RRAM 技术来应对这些挑战。我们系统地设计了三层金属氧化物堆叠，并研究了具有不同厚度和氧空位分布的 RRAM 的开关特性，以实现可靠的体开关，而无需形成任何细丝。我们演示了兆欧级的批量开关，具有高电流非线性，高达 100 级，无需顺应电流。我们开发了一个神经形态计算内存平台，并通过实施用于自主导航/赛车任务的尖峰神经网络来展示边缘计算。我们的工作解决了现有 RRAM 技术带来的挑战，并为严格尺寸、重量和功耗限制下的边缘神经形态计算铺平了道路。