File:Prototype of asynchronous sparse binary identification transmission (ASBIT) system-on-chip wireless ASIC with on-chip oscillator, coil antenna and digital logic circuit.webp

English: "a, Principal circuit blocks of the ASIC, with an on-chip antenna coil, rectifier, digital logic circuit and oscillator as the system-on-chip clock. b, Microphotograph of wireless ASIC prototype chips on a fingertip as an illustration of size, with inset showing the chip layout. c, Photograph of the benchtop three-coil antenna configuration in wireless experiments (through path 9 mm in air, 1 mm glass). d, Left, combining measurements with simulations to study the scalability of the ASBIT network, with initial backscattered signals measured from 78 fabricated chips (here, with an average SNR of 1.7 dB). A larger-scale network was generated by simulating data backscattered from the equivalent of 200, 500 and 1,000 sensor nodes. Right, transient outputs of the matched filters (labelled M.F.) for each case considered in d (78, 200, 500 and 1,000 nodes). The outputs from the matched filters for data received are shown for three randomly chosen chips, a, b and c, obtained using three different sets of matched filters that were individually calibrated for each chip. e, Average EER (n = 40, 6 s simulation epoch) as a function of the number of nodes added to the network. Each ‘background’ node detecting 10, 25 or 50 asynchronous events per second, respectively. Sparsity in event detection in the background enables a considerably larger number of nodes to operate in the network. f, Average EER as a function of the number of nodes and matched filters used for event recovery to account for the clock drift in each chip. The plot also shows how a longer demodulation time per node is required with an increased number of matched filters (dashed line; 1 s data). Osc., oscillator; clk, clock; vsw, switching voltage; mod., modulator." "Networks of spatially distributed radiofrequency identification sensors could be used to collect data in wearable or implantable biomedical applications. However, the development of scalable networks remains challenging. Here we report a wireless radiofrequency network approach that can capture sparse event-driven data from large populations of spatially distributed autonomous microsensors. We use a spectrally efficient, low-error-rate asynchronous networking concept based on a code-division multiple-access method. We experimentally demonstrate the network performance of several dozen submillimetre-sized silicon microchips and complement this with large-scale in silico simulations. To test the notion that spike-based wireless communication can be matched with downstream sensor population analysis by neuromorphic computing techniques, we use a spiking neural network machine learning model to decode prerecorded open source data from eight thousand spiking neurons in the primate cortex for accurate prediction of hand movement in a cursor control task."