Abstract:
A memory device that includes a plurality of ROM cells each having spaced apart source and drain regions formed in a substrate with a channel region therebetween, a first gate disposed over and insulated from a first portion of the channel region, a second gate disposed over and insulated from a second portion of the channel region, and a conductive line extending over the plurality of ROM cells. The conductive line is electrically coupled to the drain regions of a first subgroup of the ROM cells, and is not electrically coupled to the drain regions of a second subgroup of the ROM cells. Alternately, a first subgroup of the ROM cells each includes a higher voltage threshold implant region in the channel region, whereas a second subgroup of the ROM cells each lack any higher voltage threshold implant region in the channel region.
Abstract:
A memory device that includes a plurality of ROM cells each having spaced apart source and drain regions formed in a substrate with a channel region therebetween, a first gate disposed over and insulated from a first portion of the channel region, a second gate disposed over and insulated from a second portion of the channel region, and a conductive line extending over the plurality of ROM cells. The conductive line is electrically coupled to the drain regions of a first subgroup of the ROM cells, and is not electrically coupled to the drain regions of a second subgroup of the ROM cells. Alternately, a first subgroup of the ROM cells each includes a higher voltage threshold implant region in the channel region, whereas a second subgroup of the ROM cells each lack any higher voltage threshold implant region in the channel region.
Abstract:
A method of forming a memory device on a semiconductor substrate having a memory region (with floating and control gates), a first logic region (with first logic gates) and a second logic region (with second logic gates). A first implantation forms the source regions adjacent the floating gates in the memory region, and the source and drain regions adjacent the first logic gates in the first logic region. A second implantation forms the source and drain regions adjacent the second logic gates in the second logic region. A third implantation forms the drain regions adjacent the control gates in the memory region, and enhances the source region in the memory region and the source/drain regions in the first logic region. A fourth implantation enhances the source/drain regions in the second logic region.
Abstract:
Numerous examples of summing circuits for a neural network are disclosed. In one example, a circuit for summing current received from a plurality of synapses in a neural network comprises a voltage source; a load coupled between the voltage source and an output node; a voltage clamp coupled to the output node for maintaining a voltage at the output node; and a plurality of synapses coupled between the output node and ground; wherein an output current flows through the output node, the output current equal to a sum of currents drawn by the plurality of synapses.
Abstract:
Numerous embodiments of programming, verifying, and reading systems and methods for use with a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. Selected cells can be programmed and verified with extreme precision to hold one of N different values. During a read operation, the system determines which of the N different values is stored in a selected cell.
Abstract:
Numerous examples are disclosed for verifying a weight programmed into a selected non-volatile memory cell in a neural memory. In one example, a circuit comprises a digital-to-analog converter to convert a target weight comprising digital bits into a target voltage, a current-to-voltage converter to convert an output current from the selected non-volatile memory cell during a verify operation into an output voltage, and a comparator to compare the output voltage to the target voltage during a verify operation.
Abstract:
A number of circuits for use in an output block coupled to a non-volatile memory array in a neural network are disclosed. The embodiments include a circuit for converting an output current from a neuron in a neural network into an output voltage, a circuit for converting a voltage received on an input node into an output current, a circuit for summing current received from a plurality of neurons in a neural network, and a circuit for summing current received from a plurality of neurons in a neural network.
Abstract:
Numerous embodiments of a precision programming algorithm and apparatus are disclosed for precisely and quickly depositing the correct amount of charge on the floating gate of a non-volatile memory cell within a vector-by-matrix multiplication (VMM) array in an artificial neural network. Selected cells thereby can be programmed with extreme precision to hold one of N different values.
Abstract:
Two or more physical memory cells are grouped together to form a logical cell that stores one of N possible levels. Within each logical cell, the memory cells can be programmed using different mechanisms. For example, one or more of the memory cells in a logical cell can be programmed using a coarse programming mechanism, one or more of the memory cells can be programmed using a fine mechanism, and one or more of the memory cells can be programmed using a tuning mechanism. This achieves extreme programming accuracy and programming speed.