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US Patent 11790219 Three dimensional circuit implementing machine trained network

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Contents

Is a
Patent
Patent

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11790219
Patent Inventor Names
Kenneth Duong
Steven L. Teig
Date of Patent
October 17, 2023
Patent Application Number
17500374
Date Filed
October 13, 2021
Patent Citations
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US Patent 9142262 Stacked semiconductor device
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US Patent 11011494 Layer structures for making direct metal-to-metal bonds at low temperatures in microelectronics
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US Patent 11011503 Direct-bonded optoelectronic interconnect for high-density integrated photonics
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US Patent 11031285 Diffusion barrier collar for interconnects
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US Patent 11037919 Techniques for processing devices
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US Patent 11056348 Bonding surfaces for microelectronics
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US Patent 11069734 Image sensor device
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US Patent 11176450 Three dimensional circuit implementing machine trained network
...
Patent Primary Examiner
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Mamadou L Diallo
CPC Code
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H01L 2225/06513
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H01L 2225/06517
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H01L 2225/06565
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H01L 2225/06586
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G06N 3/08
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G06N 3/04
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G06F 2201/85
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H03K 19/21
...
Patent abstract

Some embodiments provide a three-dimensional (3D) circuit structure that has two or more vertically stacked bonded layers with a machine-trained network on at least one bonded layer. As described above, each bonded layer can be an IC die or an IC wafer in some embodiments with different embodiments encompassing different combinations of wafers and dies for the different bonded layers. The machine-trained network in some embodiments includes several stages of machine-trained processing nodes with routing fabric that supplies the outputs of earlier stage nodes to drive the inputs of later stage nodes. In some embodiments, the machine-trained network is a neural network and the processing nodes are neurons of the neural network. In some embodiments, one or more parameters associated with each processing node (e.g., each neuron) is defined through machine-trained processes that define the values of these parameters in order to allow the machine-trained network (e.g., neural network) to perform particular operations (e.g., face recognition, voice recognition, etc.). For example, in some embodiments, the machine-trained parameters are weight values that are used to aggregate (e.g., to sum) several output values of several earlier stage processing nodes to produce an input value for a later stage processing node.

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