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Abstract With the fast growth of the number of electronic devices on the internet of things (IoT), hardware‐based security primitives such as physically unclonable functions (PUFs) have emerged to overcome the shortcomings of conventional software‐based cryptographic technology. Existing PUFs exploit manufacturing process variations in a semiconductor foundry technology. This results in a static challenge–response behavior, which can present a long‐term security risk. This study shows a reconfigurable PUF based on nanoscale magnetic tunnel junction (MTJ) arrays that uses stochastic dynamics induced by voltage‐controlled magnetic anisotropy (VCMA) for true random bit generation. A total of 100 PUF instances are implemented using 10 ns voltage pulses on a single chip with a 10 × 10 MTJ array. The unipolar nature of the VCMA mechanism is exploited to stabilize the MTJ state and eliminate bit errors during readout. All PUF instances show entropy close to one, inter‐Hamming distance close to 50%, and no bit errors in 10⁴repeated readout measurements. 

As data‐intensive applications increasingly strain conventional computing systems, processing‐in‐memory (PIM) has emerged as a promising paradigm to alleviate the memory wall by minimizing data transfer between memory and processing units. This review presents the recent advances in both stateful and non‐stateful logic techniques for PIM, focusing on emerging nonvolatile memory technologies such as resistive random‐access memory (RRAM), phase‐change memory (PCM), and magnetoresistive random‐access memory (MRAM). Both experimentally demonstrated and simulated logic designs are critically examined, highlighting key challenges in reliability and the role of device‐level optimization in enabling scalable and commercial viable PIM systems. The review begins with an overview of relevant logic families, memristive device types, and associated reliability metrics. Each logic family is then explored in terms of how it capitalizes on distinct device properties to implement logic techniques. A comparative table of representative device stacks and performance parameters illustrates trade‐offs and quality indicators. Through this comprehensive analysis, the development of optimized, robust memristive devices for next‐generation PIM applications is supported. 

We introduce Focal Split, a handheld, snapshot depth camera with fully onboard power and computing based on depth-from-differential-defocus (DfDD). Focal Split is passive, avoiding power consumption of light sources. Its achromatic optical system simultaneously forms two differentially defocused images of the scene, which can be independently captured using two photosensors in a snapshot. The data processing is based on the DfDD theory, which efficiently computes a depth and a confidence value for each pixel with only 500 floating point operations (FLOPs) per pixel from the camera measurements. We demonstrate a Focal Split prototype, which comprises a handheld custom camera system connected to a Raspberry Pi 5 for real-time data processing. The system consumes 4.9 W and is powered on a 5 V, 10,000 mAh battery. The prototype can measure objects with distances from 0.4 m to 1.2 m, outputting 480×360 sparse depth maps at 2.1 frames per second (FPS) using unoptimized Python scripts. Focal Split is DIY friendly. A comprehensive guide to building your own Focal Split depth camera, code, and additional data can be found at https://focal-split.qiguo.org.