Ferroelectric MirrorBit-Integrated Field-Programmable Memory Array for TCAM, Storage, and In-Memory Computing Applications
Paritosh Meihar, Rowtu Srinu, Sandip Lashkare, Ajay Kumar Singh, Halid, Mulaosmanovic, Veeresh Deshpande, Stefan D\"unkel, Sven Beyer, Udayan, Ganguly

TL;DR
This paper introduces a reconfigurable ferroelectric MirrorBit memory array that combines multi-state storage, fast operation, and reconfigurability for efficient in-memory and associative computing applications.
Contribution
It presents a novel ferroelectric MirrorBit-based memory architecture with multi-state capability and reconfigurability, enabling versatile in-memory computing functions.
Findings
Demonstrated 4-state (2-bit) memory using polarization gradients.
Showed reconfigurable array switching between AND and NOR logic modes.
Enabled fast write, dense storage, and efficient search in a unified architecture.
Abstract
In-memory computing on a reconfigurable architecture is the emerging field which performs an application-based resource allocation for computational efficiency and energy optimization. In this work, we propose a Ferroelectric MirrorBit-integrated field-programmable reconfigurable memory. We show the conventional 1-Bit FeFET, the MirrorBit, and MirrorBit-based Ternary Content-addressable memory (MCAM or MirrorBit-based TCAM) within the same field-programmable array. Apart from the conventional uniform Up and Down polarization states, the additional states in the MirrorBit are programmed by applying a non-uniform electric field along the transverse direction, which produces a gradient in the polarization and the conduction band energy. This creates two additional states, thereby, creating a total of 4 states or 2-bit of information. The gradient in the conduction band resembles a Schottky…
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Taxonomy
TopicsFerroelectric and Negative Capacitance Devices · Advanced Memory and Neural Computing · Semiconductor materials and devices
MethodsClass-activation map · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
