Voltage-Summation-Based Compute-in-Memory Technology with Capacitive Synaptic Devices

Previous compute-in-memory (CIM) technologies use current summation for executing matrix–vector multiplication (MVM) operations and are gaining attention as next-generation artificial intelligence (AI) computing systems. Despite their advantages, these technologies encounter significant difficulties, such as managing wide current ranges in large arrays, overcoming substantial “IR (current-resistance)-drop” issues, and integrating with complementary metal–oxide–semiconductor operating circuits. Herein, an innovative voltage-summation-based CIM (V-CIM) technology is introduced; this technology represents a paradigm shift that employs capacitive coupling at a floating voltage summation node for MVM operations. By using a differential pair of input voltages (|ΔV|) with opposite signs relative to the reference voltage, the accumulated MVM results at the floating summation node cancel out, eliminating IR-drop. Representing the MVM result as an analog voltage simplifies the neuron circuit design and significantly reduces energy consumption. V-CIM technology is reported with two types of capacitive synaptic devices: read-only and programmable. A prototype V-CIM chip using read-only capacitors is developed, demonstrating the superiority of V-CIM in image classification applications. For programmable capacitive synaptic devices, a memcapacitor device based on logic-compatible embedded flash memory is introduced. The work sheds light on ultralow-power, highly integrated, and highly reliable AI computing systems based on CIM technology.