We demonstrate interface-enhanced memristors (OxReRAM) tailored for cryogenic spin-qubit control. By engineering a sparse filament network, our devices achieve eight nonvolatile resistance levels with an ultra-low read noise rate of around 0.3 %. When embedded in a cryogenic gain stage with R_L = 30 kΩ and V_in = 0.3 V, it will deliver a ±1 V output range and sub-100-μV resolution using only six memristors per channel. This single-line biasing architecture will reduce wires, paving the way for large-scalce quantum processors.

Analog computation-in-memory (CIM) architecture alleviates massive data movement between the memory and the processor, thus promising great prospects to accelerate certain computational tasks in an energy-efficient manner. However, data converters involved in these architectures typically achieve the required computing accuracy at the expense of high area and energy footprint which can potentially determine CIM candidacy for low-power and compact edge-AI devices. In this work, we present a memory-periphery co-design to perform accurate A/D conversions of analog matrix-vector-multiplication (MVM) outputs. Here, we introduce a scheme where select-lines and bit-lines in the memory are virtually fixed to improve conversion accuracy and aid a ring-oscillator-based A/D conversion, equipped with component sharing and inter-matching of the reference blocks. In addition, we deploy a self-timed technique to further ensure high robustness addressing global design and cycle-to-cycle variations. Based on measurement results of a 4Kb CIM chip prototype equipped with TSMC 40nm, a relative accuracy of up to 99.71% is achieved with an energy efficiency of 115.1 TOPS/W and computational density of 12.1 TOPS/mm2 for the MNIST dataset. Thus, an improvement of up to 11.3X and 7.5X compared to the state-of-the-art, respectively.