For leveraging wearable technologies to advance precision medicine, personalized and learning-based analysis of continuously acquired health data is indispensable, for which neuromorphic computing provides the most efficient implementation of artificial intelligence (AI) data processing. For realizing on-body neuromorphic computing, skin-like stretchability is required but has yet to be combined with the desired neuromorphic metrics, including linear symmetric weight update and sufficient state retention, for achieving high computing efficiency. Here, we report an intrinsically stretchable electrochemical transistor-based neuromorphic device, which provides a large number (>800) of states, linear/symmetric weight update, excellent switching endurance (>100 million), and good state retention (>10⁴ s) together with the high stretchability of 100% strain. We further demonstrate a prototype neuromorphic array that can perform vector-matrix multiplication even at 100% strain and also the feasibility of implementing AI-based classification of health signals with a high accuracy that is minimally influenced by the stretched state of the neuromorphic hardware.

为了利用可穿戴技术推进精准医疗，对连续获取的健康数据进行个性化和基于学习的分析是必不可少的，神经形态计算为此提供了最有效的人工智能 (AI) 数据处理实现。为了实现在体神经形态计算，需要类似皮肤的可拉伸性，但尚未与所需的神经形态指标相结合，包括线性对称权重更新和足够的状态保留，以实现高计算效率。在这里，我们报告了一种本质上可拉伸的基于电化学晶体管的神经形态装置，它提供了大量 (>800) 状态、线性/对称权重更新、出色的切换耐久性 (>1 亿) 和良好的状态保持 (>10 ⁴s) 以及 100% 应变的高拉伸性。我们进一步展示了一个原型神经形态阵列，即使在 100% 应变下也可以执行向量矩阵乘法，并且还展示了实现基于 AI 的健康信号分类的可行性，其准确度不受神经形态硬件拉伸状态的影响。