As quantum processors grow in complexity, new challenges arise such as the management of device variability and the interface with supporting electronics. Spin qubits in silicon quantum dots can potentially address these challenges given their control fidelities and potential for compatibility with large-scale integration. Here we report the integration of 1,024 independent silicon quantum dot devices with on-chip digital and analogue electronics, all operating below 1 K. A high-frequency analogue multiplexer provides fast access to all devices with minimal electrical connections, allowing characteristic data across the quantum dot array to be acquired and analysed in under 10 min. This is achieved by leveraging radio-frequency reflectometry with state-of-the-art signal integrity, characterized by a typical signal-to-noise voltage ratio in excess of 75 for an integration time of 3.18 μs. We extract key quantum dot parameters by automated machine learning routines to assess quantum dot yield and understand the impact of device design. We find correlations between quantum dot parameters and room-temperature transistor behaviour that could be used as a proxy for in-line process monitoring.

随着量子处理器的复杂性增加，新的挑战也随之出现，例如设备可变性的管理以及与支持电子设备的接口。鉴于硅量子点中的自旋量子比特的控制保真度以及与大规模集成兼容的潜力，它们有可能解决这些挑战。在这里，我们报告了 1,024 个独立硅量子点器件与片上数字和模拟电子器件的集成，所有这些器件都在 1 K 以下运行。高频模拟多路复用器以最少的电气连接提供对所有设备的快速访问，从而可以在 10 分钟内采集和分析整个量子点阵列的特征数据。这是通过利用具有先进信号完整性的射频反射法实现的，其特点是典型的信噪电压比超过 75，积分时间为 3.18 μs。我们通过自动化机器学习例程提取关键量子点参数，以评估量子点良率并了解器件设计的影响。我们发现了量子点参数和室温晶体管行为之间的相关性，可以用作在线过程监控的代理。