BEOL-Embedded 3D Polylithic Integration: Thermal and Interconnection Considerations

Kaul, Ankit; Rajan, Sreejith Kochupurackal; Hossen, Obaidul; May, G.S.; Bakir, Muhannad S.

doi:10.1109/ectc32862.2020.00231

Cited by 12 publications

(1 citation statement)

References 22 publications

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“…To be specific, we considered the transistor parameter degradation ratio between the top tier and the bottom substrate of the silicon transistors, as shown in the experimental data for the top tier laser-recrystallized silicon transistors [43]. Furthermore, we also considered the heat dissipation in M3D architecture, by introducing a compact model of the thermal profile [44] into the M3D benchmark framework accounting for (a) top and bottom tiers' power density; (b) the chip area and substrate thickness; and (c) inter-tier BEOL thickness and material. Fig.…”

Section: Monolithic 3d Integrationmentioning

confidence: 99%

RRAM for Compute-in-Memory: From Inference to Training

Shim

Peng

et al. 2021

IEEE Trans. Circuits Syst. I

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To efficiently deploy machine learning applications to the edge, compute-in-memory (CIM) based hardware accelerator is a promising solution with improved throughput and energy efficiency. Instant-on inference is further enabled by emerging non-volatile memory technologies such as resistive random access memory (RRAM). This paper reviews the recent progresses of the RRAM based CIM accelerator design. First, the multilevel states RRAM characteristics are measured from a test vehicle to examine the key device properties for inference. Second, a benchmark is performed to study the scalability of the RRAM CIM inference engine and the feasibility towards monolithic 3D integration that stacks RRAM arrays on top of advanced logic process node. Third, grand challenges associated with in-situ training are presented. To support accurate and fast in-situ training and enable subsequent inference in an integrated platform, a hybrid precision synapse that combines RRAM with volatile memory (e.g. capacitor) is designed and evaluated at system-level. Prospects and future research needs are discussed.

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Section: Monolithic 3d Integrationmentioning

confidence: 99%