Analogue in-memory computing using memristors could alleviate the performance constraints imposed by digital von Neumann systems in data-intensive tasks. Conventional linear memristors typically operate at high currents, potentially limiting power efficiency and scalability in practical applications. Here, we show that nonlinear ferroelectric tunnel junction memristors can perform linear computation at ultralow currents. Using logarithmic line drivers, we demonstrate that analogue-voltage-amplitude vector–matrix multiplication (VMM) can be performed in selectorless ferroelectric tunnel junction crossbars by exploiting a device nonlinearity factor that remains constant for multiple conductive states. We also show that our ferroelectric tunnel junction crossbars have the attributes required to scale analogue VMM-intensive applications, such as neural inference engines, towards energy efficiencies above 100 tera-operations per second per watt.

使用忆阻器的模拟内存中计算可以减轻数字冯·诺依曼系统在数据密集型任务中的性能限制。传统的线性忆阻器通常在高电流下工作，在实际应用中可能会限制电源效率和可扩展性。在这里，我们证明了非线性铁电隧道结忆阻器可以在超低电流下执行线性计算。使用对数线驱动器，我们证明了可以通过利用在多个导电状态下保持不变的器件非线性因子，在无选择器铁电隧道结交叉开关中执行模拟电压幅度矢量矩阵乘法（VMM）。我们还表明，我们的铁电隧道结交叉开关具有扩展模拟VMM密集型应用所需的属性，