惠普Nature：面向神经形态工程的三阶纳米电路元件

文章链接：https://doi.org/10.1038/s41586-020-2735-5

背景

不断增长的海量数据和计算需求，以及传统晶体管计算系统的性能趋于饱和，激发了人们对新型的计算基元的兴趣。目前仿生或神经形态人工智能的硬件方法主要依赖于复杂的晶体管电路来模拟生物功能，能够模拟神经元功能的单一的电子元件还未出现。周期尖峰只需要二阶复杂度，但在电路元件中产生神经形态动作电位理论上需要最小的三阶复杂度。而基于晶体管的数字芯片在模拟代表神经元丰富的非线性动力学的复杂方程的过程中会变得复杂、庞大且能量低下。

摘要

目前仿生或神经形态人工智能的硬件实现依赖于复杂的晶体管电路来模拟生物功能。然而，这些可以用能够自然表达神经形态非线性动力学的高阶电路元件来更真实地模拟。在电路元件中产生神经形态动作电位理论上至少需要三阶复杂度(例如，三个动态电物理过程)。然而目前很少有二阶神经形态元件的例子被报道，以前也没有任何孤立的三阶元件的演示。通过实验和模拟，其展示了利用多个电物理过程(包括莫特跃迁动力学)制备纳米级三阶电路元件。并以三个三阶元件为基础构建了简单无晶体管电路，实现了布尔运算。此外，研究人员还组建了包含24个三阶纳米元件的集成阵列，为图像分割这一计算难题提供了模拟解决方案。

Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor circuits to simulate biological functions. However, these can instead be more faithfully emulated by higher-order circuit elements that naturally express neuromorphic nonlinear dynamics. Generating neuromorphic action potentials in a circuit element theoretically requires a minimum of third-order complexity (for example, three dynamical electrophysical processes) been few examples of second-order neuromorphic elements, and no previous demonstration of any isolated third-order element. Using both experiments and modelling, here we show how multiple electrophysical processes—including Mott transition dynamics—form a nanoscale third-order circuit element. We demonstrate simple transistorless networks of third-order elements that perform Boolean operations and find analogue solutions to a computationally hard graph-partitioning problem. This work paves a way towards very compact and densely functional neuromorphic computing primitives, and energy-efficient validation of neuroscientific models.

图1. 三阶纳米元件结构与表征

图2. 动作电位的实验测量与模拟