An official website of the United States government
Abstract In‐sensor computing is an emerging architectural paradigm that fuses data acquisition and processing within a sensory domain. The integration of multiple functions into a single domain reduces the system footprint while it minimizes the energy and time for data transfer between sensory and computing units. However, it is challenging for a simple and compact image sensor array to achieve both sensing and computing in each pixel. Here, this work demonstrates a focal plane array with a heterogeneously integrated one‐photodiode one‐resistor (1P‐1R)‐based artificial optical neuron that emulates the sensing, computing, and memorization of a biological retina system. This work employs an InGaAs photodiode featuring a high responsivity and a broad spectrum that covers near‐infrared (NIR) signals and employs an HfO2memristor as the artificial synapse to achieve the computing/memorization in an analog domain. Using the fabricated focal plane array integrated with an artificial neural network, this work performs in‐sensor image identification of finger veins driven by NIR light illumination (≈84 % accuracy). The proposed in‐sensor image computing architecture that broadly covers the NIR spectrum offers widespread application of focal plane array for computer vision, neuromorphic computing, biomedical engineering, etc.
more »
« less
Curved neuromorphic image sensor array using a MoS2-organic heterostructure inspired by the human visual recognition system
Abstract Conventional imaging and recognition systems require an extensive amount of data storage, pre-processing, and chip-to-chip communications as well as aberration-proof light focusing with multiple lenses for recognizing an object from massive optical inputs. This is because separate chips (i.e., flat image sensor array, memory device, and CPU) in conjunction with complicated optics should capture, store, and process massive image information independently. In contrast, human vision employs a highly efficient imaging and recognition process. Here, inspired by the human visual recognition system, we present a novel imaging device for efficient image acquisition and data pre-processing by conferring the neuromorphic data processing function on a curved image sensor array. The curved neuromorphic image sensor array is based on a heterostructure of MoS2and poly(1,3,5-trimethyl-1,3,5-trivinyl cyclotrisiloxane). The curved neuromorphic image sensor array features photon-triggered synaptic plasticity owing to its quasi-linear time-dependent photocurrent generation and prolonged photocurrent decay, originated from charge trapping in the MoS2-organic vertical stack. The curved neuromorphic image sensor array integrated with a plano-convex lens derives a pre-processed image from a set of noisy optical inputs without redundant data storage, processing, and communications as well as without complex optics. The proposed imaging device can substantially improve efficiency of the image acquisition and recognition process, a step forward to the next generation machine vision.
more »
« less
- PAR ID:
- 10370561
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract As machine vision technology generates large amounts of data from sensors, it requires efficient computational systems for visual cognitive processing. Recently, in-sensor computing systems have emerged as a potential solution for reducing unnecessary data transfer and realizing fast and energy-efficient visual cognitive processing. However, they still lack the capability to process stored images directly within the sensor. Here, we demonstrate a heterogeneously integrated 1-photodiode and 1 memristor (1P-1R) crossbar for in-sensor visual cognitive processing, emulating a mammalian image encoding process to extract features from the input images. Unlike other neuromorphic vision processes, the trained weight values are applied as an input voltage to the image-saved crossbar array instead of storing the weight value in the memristors, realizing the in-sensor computing paradigm. We believe the heterogeneously integrated in-sensor computing platform provides an advanced architecture for real-time and data-intensive machine-vision applications via bio-stimulus domain reduction.more » « less
-
Vision processing on traditional architectures is inefficient due to energy-expensive off-chip data movement. Many researchers advocate pushing processing close to the sensor to substantially reduce data movement. However, continuous near-sensor processing raises sensor temperature, impairing imaging/vision fidelity. We characterize the thermal implications of using 3D stacked image sensors with near-sensor vision processing units. Our characterization reveals that near-sensor processing reduces system power but degrades image quality. For reasonable image fidelity, the sensor temperature needs to stay below a threshold, situationally determined by application needs. Fortunately, our characterization also identifies opportunities—unique to the needs of near-sensor processing—to regulate temperature based on dynamic visual task requirements and rapidly increase capture quality on demand. Based on our characterization, we propose and investigate two thermal management strategies—stop-capture-go and seasonal migration—for imaging-aware thermal management. For our evaluated tasks, our policies save up to 53% of system power with negligible performance impact and sustained image fidelity.more » « less
-
Fast electrochemical imaging enables the dynamic study of electroactive molecule diffusion in neurotransmitter release from single cells and dopamine mapping in brain slices. In this paper, we discuss the design of an electrochemical imaging sensor using a monolithic CMOS sensor array and a multifunctional data acquisition system. Using post-CMOS fabrication, the CMOS sensor integrates 1024 on-chip electrodes on the surface and contains 1024 low-noise amplifiers to simultaneous process parallel electrochemical recordings. Each electrochemical electrode and amplifier are optimized to operate at 10.38 kHz sampling rate. To support the operation of the high-throughput CMOS device, a multifunctional data acquisition device is developed to provide the required speed and accuracy. The high analog data rate of 10.63 MHz from all 1024 amplifiers is redundantly sampled by the custom-designed data acquisition system which can process up to 73.6 MHz with up to ~400 Mbytes/s data rate to a computer using USB 3.0 interface. To contain the liquid above the electrochemical sensors and prevent electronic and wire damage, we packaged the monolithic sensor using a 3D-printed well. Using the presented device, 32 pixel × 32 pixel electrochemical imaging of dopamine diffusion is successfully demonstrated at over 10,000 frames per second, the fastest reported to date.more » « less
-
We report the implementation of a fully on-chip, lensless microscopy technique termed optofluidic ptychography. This imaging modality complements the miniaturization provided by microfluidics and allows the integration of ptychographic microscopy into various lab-on-a-chip devices. In our prototype, we place a microfluidic channel on the top surface of a coverslip and coat the bottom surface with a scattering layer. The channel and the coated coverslip substrate are then placed on top of an image sensor for diffraction data acquisition. Similar to the operation of a flow cytometer, the device utilizes microfluidic flow to deliver specimens across the channel. The diffracted light from the flowing objects is modulated by the scattering layer and recorded by the image sensor for ptychographic reconstruction, where high-resolution quantitative complex images are recovered from the diffraction measurements. By using an image sensor with a 1.85 μm pixel size, our device can resolve the 550 nm linewidth on the resolution target. We validate the device by imaging different types of biospecimens, including C. elegans , yeast cells, paramecium , and closterium sp . We also demonstrate a high-resolution ptychographic reconstruction at a video framerate of 30 frames per second. The reported technique can address a wide range of biomedical needs and engenders new ptychographic imaging innovations in a flow cytometer configuration.more » « less
