2D semiconductors, especially transition metal dichalcogenide (TMD) monolayers, are extensively studied for electronic and optoelectronic applications. Beyond intensive studies on single transistors and photodetectors, the recent advent of large‐area synthesis of these atomically thin layers has paved the way for 2D integrated circuits, such as digital logic circuits and image sensors, achieving an integration level of ≈100 devices thus far. Here, a decisive advance in 2D integrated circuits is reported, where the device integration scale is increased by tenfold and the functional complexity of 2D electronics is propelled to an unprecedented level. Concretely, an analog optoelectronic processor inspired by biological vision is developed, where 32 × 32 = 1024 MoS2 photosensitive field‐effect transistors manifesting persistent photoconductivity (PPC) effects are arranged in a crossbar array. This optoelectronic processor with PPC memory mimics two core functions of human vision: it captures and stores an optical image into electrical data, like the eye and optic nerve chain, and then recognizes this electrical form of the captured image, like the brain, by executing analog in‐memory neural net computing. In the highlight demonstration, the MoS2 FET crossbar array optically images 1000 handwritten digits and electrically recognizes these imaged data with 94% accuracy.

Complementary metal-oxide-semiconductor (CMOS) image sensors are a visual outpost of many machines that interact with the world. While they presently separate image capture in front-end silicon photodiode arrays from image processing in digital back-ends, efforts to process images within the photodiode array itself are rapidly emerging, in hopes of minimizing the data transfer between sensing and computing, and the associated overhead in energy and bandwidth. Electrical modulation, or programming, of photocurrents is requisite for such in-sensor computing, which was indeed demonstrated with electrostatically doped, but non-silicon, photodiodes. CMOS image sensors are currently incapable of in-sensor computing, as their chemically doped photodiodes cannot produce electrically tunable photocurrents. Here we report in-sensor computing with an array of electrostatically doped silicon p-i-n photodiodes, which is amenable to seamless integration with the rest of the CMOS image sensor electronics. This silicon-based approach could more rapidly bring in-sensor computing to the real world due to its compatibility with the mainstream CMOS electronics industry. Our wafer-scale production of thousands of silicon photodiodes using standard fabrication emphasizes this compatibility. We then demonstrate in-sensor processing of optical images using a variety of convolutional lters electrically programmed into a 3 × 3 network of these photodiodes. Main TextComplementary metal oxide semiconductor (CMOS) image sensors have become an indispensable part of our data-driven world, where visual information prevails 1,2 . The front-end silicon photodiode array in a CMOS image sensor converts light into electrical currents. These electrical data undergo analog-to-digital conversion and are then shuttled to a digital back-end for image processing. While this standard sequence of front-end image capture and back-end processing restricts the role of the photodiode array to sensing, emerging machine vision applications would bene t from data processing within the photodiode array itself 3,4 . For example, in object tracking for self-driving vehicles, drones, or robots, where only the edges of objects are relevant [5][6][7][8] , edge extraction in the front-end photodiode array would be much more economical in energy expenditure, processing latency, required bandwidth, and memory usage, as compared to transferring the whole image data containing super uous information to the back-end digital processor-only to extract the edges 9 . Such in-sensor computing would require an electrical modulation, or programming, of photocurrents. In fact, in-sensor computing has been recently demonstrated with electrostatically doped photodiodes whose photocurrents can be modulated with gate biasing 10,11 . These pioneering works have realized electrostatically doped photodiodes by gating two-dimensional (2D) transition metal dichalcogenide (TMD) layers or their van der Waals (vdW) stacks [12][13][14] . In contrast, such in-sensor computing is not possible with ...

pH controls a large repertoire of chemical and biochemical processes in water. Densely arrayed pH microenvironments would parallelize these processes, enabling their high-throughput studies and applications. However, pH localization, let alone its arrayed realization, remains challenging because of fast diffusion of protons in water. Here, we demonstrate arrayed localizations of picoliter-scale aqueous acids, using a 256-electrochemical cell array defined on and operated by a complementary metal oxide semiconductor (CMOS)–integrated circuit. Each cell, comprising a concentric pair of cathode and anode with their current injections controlled with a sub-nanoampere resolution by the CMOS electronics, creates a local pH environment, or a pH “voxel,” via confined electrochemistry. The system also monitors the spatiotemporal pH profile across the array in real time for precision pH control. We highlight the utility of this CMOS pH localizer-imager for high-throughput tasks by parallelizing pH-gated molecular state encoding and pH-regulated enzymatic DNA elongation at any selected set of cells.

We present the rationale, implementation and performance features of a virtual lab environment for an electronic circuits course. The primary purpose of the tool is to aid the student in learning debugging techniques by providing an environment that emulates some of the failure modes of a real lab. The tool is implemented as a Java application.

The Internet and its applications in education and industry have significantly influenced how we teach and learn. This has all occurred as a consequence of emerging technologies and the demands for online instruction by consumers. In the midst of this environment of rapid growth, a new form of pedagogy has emerged. However, much of it is not the result of research. This paper addresses the need for a conceptual approach to researching, e-learning instructional design and the technologies employed as a basis for e-learning. A programmatic research construct is offered as a structure for building a conceptual model. Three categories of variables are considered in building the construct. They include outcome, in situ, and independent variables. The intent of the paper is to engage researchers and developers in a process of further defining the variables and translating them into research questions that might serve as guidelines in building the literature base for the pedagogy of online instruction.