True random numbers are essential in various research and engineering problems. Their generation depends upon a robust physical entropy noise. Here, we present true random number generation by harnessing the conductance noise probed in structurally metastable 1T' molybdenum ditelluride (MoTe2). The noise, well-fitting a Poisson process, is proved a robust physical entropy noise at low and even cryogenic temperatures. Noise characteristic analysis suggests the noise may originate from the polarization variations of the underlying ferroelectric dipoles in 1T' MoTe2. We demonstrate the noise allows for true random number generation, enabling their use as seed for generating high-throughput secure random numbers exceeding 1 Mbit/s, appealing for practical applications in, for instance, cryptography where data security is now a severe issue. As an example, we show biometric information safeguarding in neural networks by using the random numbers as mask, proving a promising data security measure in big data and artificial intelligence.

翻译：真随机数在各类研究与工程问题中至关重要，其生成依赖于稳健的物理熵噪声。本文提出通过利用在结构亚稳态1T'二碲化钼（MoTe2）中探测到的电导噪声来实现真随机数生成。该噪声在低温和深低温环境下仍符合泊松过程，被证明是一种稳健的物理熵噪声。噪声特性分析表明，其可能源于1T' MoTe2中铁电偶极子的极化涨落。我们证明该噪声可用于生成真随机数，并能作为种子产生通量超过1 Mbit/s的高吞吐量安全随机数，适用于密码学等数据安全需求迫切的实际应用领域。作为示例，我们通过将随机数作为掩码应用于神经网络中的生物特征信息保护，证明了该技术在大数据与人工智能领域是一种极具前景的数据安全方案。