Monolithic 3D Integration of Vertical Memory with Phototransistor for Near-Sensor Cryptography and Homomorphic Data Searching.

Abstract

Inspired by the human retina, retinomorphic systems achieve efficient near-sensor processing by tightly integrating sensing, memory, and computing. However, unlike biological vision, which evolved without selective pressure for data confidentiality, artificial edge systems face critical security demands. Therefore, next-generation hardware must extend beyond biological mimicry by combining bio-inspired efficiency with cryptographic capabilities. Here, a compact, multifunctional wafer-scale monolithic 3D (M3D) architecture is proposed for secure in-memory processing of optically acquired visual data. Integrating quantum dot-sensitized phototransistors with stacked high-density vertical resistive random-access memories (VRRAMs) provides multi-domain entropy sources, generating physical unclonable function (PUF) keys with ≈50% inter-device variability. Multi-layer encryption using functionally independent PUF keys enhances cryptographic resilience through key diversity. Concurrently, M3D ternary content-addressable memory (TCAM) array, implemented with wide-bandgap IGZO transistors, achieves high sensing margin (≈1.58 × 10⁵), along with 9.61× area efficiency and 6.25× energy-delay product improvements over planar designs. Notably, M3D sensory and TCAM systems support near-sensor hashing and in-memory Hamming distance computation directly on encrypted data, enabling application-specific homomorphism with a 94.1% similarity preservation rate. Comparable classification accuracy for plaintext and encrypted hash inputs further underscores the potential of M3D-integrated platforms for secure, privacy-preserving machine vision at the edge.