Smart Material Implication Using Spin-Transfer Torque Magnetic Tunnel Junctions for Logic-in-Memory Computing. (August 2022)

• Record Type: Journal Article
• Authors: De Rose, Raffaele; Zanotti, Tommaso; Puglisi, Francesco Maria; Crupi, Felice; Pavan, Paolo; Lanuzza, Marco
• Is Part Of: Solid-state electronics. Volume 194(2022)
• Publication Date: 2022-08
• Subjects: STT-MTJ -- Logic-in-Memory -- Material implication -- SIMPLY -- Compact modeling; Semiconductors -- Periodicals; Semiconducteurs -- Périodiques; 621.38152
• DOI: 10.1016/j.sse.2022.108390
• Languages: English
• ISSNs: 0038-1101

Abstract

Highlights: Smart material implication (SIMPLY) logic based on nanoscale STT-MTJs. Managing tradeoff between error rate and energy consumption. Tracking the temperature dependence of STT-MTJ properties to mitigate reliability degradation. Abstract: Smart material implication (SIMPLY) logic has been recently proposed for the design of energy-efficient Logic-in-Memory (LIM) architectures based on non-volatile resistive memory devices. The SIMPLY logic is enabled by adding a comparator to the conventional IMPLY scheme. This allows performing a preliminary READ operation and hence the SET operation only in the case it is actually required. This work explores the SIMPLY logic scheme using nanoscale spin-transfer torque magnetic tunnel junction (STT-MTJ) devices. The performance of the STT-MTJ based SIMPLY architecture is analyzed by varying the load resistor and applied voltages to implement both READ and SET operations, while also investigating the effect of temperature on circuit operation. Obtained results show an existing tradeoff between error rate and energy consumption, which can be effectively managed by properly setting the values of load resistor and applied voltages. In addition, our analysis proves that tracking the temperature dependence of the MTJ properties through a proportional to absolute temperature (PTAT) reference voltage at the input of the comparator is beneficial to mitigate the reliability degradation under temperature variations.