To recover visual sensation of blind patients, we have proposed a novel three dimensionally (3D) stacked retinal prosthesis chip in which several LSI chips such as consisting of photodetector, signal processing circuit and stimulus current generator are vertically stacked and electrically connected using 3D integration technology. In this work, we developed several key process for realizing 3D stacked retinal prosthesis chip. Fine sized Cu TSV of 10 µm width and 30 µm depth was successfully formed from the back side of the thinned prosthesis chip. The prosthesis chip with the back side Cu TSVs was flip-chip bonded to Si substrate/flexible substrate through Cu/Sn micro-bumps for evaluating the feasibility of 3D integration technology.
We have newly proposed heterogeneous multi-chip module integration technologies in which MEMS and LSI chips are mounted on Si or flexible substrates using a self-assembly method. A large numbers of chips were precisely and simultaneously self-assembled and bonded onto the substrates with high alignment accuracy of approximately 400 nm. Thick MEMS and LSI chips with a thickness of more than 100 μm were electrically connected by unique lateral interconnections formed crossing over chip edges with large step height. We evaluated fundamental electrical characteristics using daisy chains formed crossing over test chips which were face-up bonded onto the substrates by the self-assembly. We obtained excellent characteristics in these daisy chains. In addition, RF test chips with amplitude shift keying (ASK) demodulator and signal processing circuits were self-assembled onto the substrates and electrically connected by the lateral interconnections. We confirmed that these test chips work well.
Keywords: heatstroke, WBGT, black globe temperature, neural network, wearable device As a method of estimating the risk of heatstroke with a wearable device, we have developed a method of calculating the wet bulb globe temperature (WBGT) by estimating the black globe temperature (T g ) only from sensors that can be mounted on a wristwatch-type device. In WBGT measurement, the conventional method requires a large sensor for measuring T g , and it has been difficult to grasp an individual's heatstroke risk. In this research, we proposed a method of estimating T g using a neural network and compared the estimation accuracy for different numbers of layers and nodes. In the T g range of 31 to 41 ℃, it was confirmed that when T g was estimated by the fully connected neural network of three layers and 20 nodes, the regression coefficient between the measured T g and the estimated T g was 0.90, indicating a high accuracy.
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