Headway Technologies, Incorporated

Magic Technologies, Inc.

Gyrfalcon Technology Inc.

Advanced Numicro Systems, Inc.

Applied Spintronics, Inc.

Chyu-Jiuh Torng

Hui-Chuan Wang

Chen-Jung Chien

Cheng Tzong Horng

Kunliang Zhang

Tom Zhong

Witold Kula

Ru-Ying Tong

Cherng-Chyi Han

Yun-Fei Li

Min Li

Po-Kang Wang

Lin Yang

Rongfu Xiao

Tong Zhao

Qi Dong

Mao-Min Chen

Daniel H Liu

Adam Zhong

Yu-Hsia Chen

Wei Cao

Wai-Ming Johnson Kan

Kochan Ju

Moris Musa Dovek

Jei-Wei Chang

Xizeng Shi

Kenlin Huang

Masanori Sakai

Liubo Hong

Kenichi Takano

Yunfei Li

Rodolfo Belen

Dan Abels

Ruying Tong

Tai Min

Yee-Chung Fu

Robert Beach

Guomin Mao

Jun Chen

Cherng Chyi Han

Guangli Liu

Rodney E Lee

Wenhan Zhang

Terry Kin Ting Ko

Rong-Fu Xiao

Yi-Ting Yao

Ruth Tong

Hualiang Yu

Terry Ko

Tom Zhang

Adam Zhang

Eiki Narumi

Hsiang-Yi Wei

Po-Kang Wan

Tong Ru-Ying

Shi Xizeng

This disclosure relates to testing of integrated artificial intelligence (AI) circuit with embedded memory to improve effective chip yield and to mapping addressable memory segments of the embedded memory to multilayer AI networks at the network level, layer level, parameter level, and bit level based on bit error rate (BER) of the addressable memory segments. The disclosed methods and systems allows for deployment of one or more multilayer AI networks in an AI circuit with sufficient model accuracy even when the embedded memory has an overall BER higher than a preferred overall threshold.

Effective chip yield for artificial intelligence integrated circuit with embedded memory

An integrated circuit includes an artificial intelligence (AI) logic and an embedded memory coupled to the AI logic and connectable to an external processor. The embedded memory includes multiple storage cells and multiple reference units. One or more reference units in the memory are selected for memory access through configuration at chip packaging level by the external processor. The external processor may execute a self-test process to select or update the one or more reference units for memory access so that the error rate of memory is below a threshold. The self-test process may be performed, via a memory initialization controller in the memory, to test and reuse the reference cells in the memory at chip level. The embedded memory may be a STT-MRAM, SOT, OST MRAM, and/or MeRAM memory.

Method of self-testing and reusing of reference cells in a memory architecture

CNN based digital IC for AI contains a number of CNN processing units. Each CNN processing unit contains CNN logic circuits operatively coupling to a memory subsystem having first and second memories. The first memory includes an array of magnetic random access memory (RAM) cells for storing weights (e.g., filter coefficients) and the second memory contains SRAM for storing input signals (e.g., imagery data). The first memory may store one-time-programming weights. The memory subsystem may contain a third memory that contains magnetic RAM cells for storing one-time-programming data for security purpose. The magnetic RAM includes STT-RAM or OST-MRAM in SLC or MLC technology.

Memory subsystem in CNN based digital IC for artificial intelligence

CNN (Cellular Neural Networks or Cellular Nonlinear Networks) based digital Integrated Circuit for artificial intelligence contains multiple CNN processing units. Each CNN processing unit contains CNN logic circuits operatively coupling to a memory subsystem having first and second memories. The first memory contains magnetic random access memory (MRAM) cells for storing weights (e.g., filter coefficients) while the second memory is for storing input signals (e.g., imagery data). The first memory may store one-time-programming weights. The memory subsystem may contain a third memory that contains MRAM cells for storing one-time-programming data for security purpose. The second memory contains MRAM cells or static random access memory cells. Each MRAM cell contains a Spin-Orbit-Torque (SOT) based magnetic tunnel junction (MTJ) element.

CNN based digital IC for AI contains a number of CNN processing units. A first CNN processing unit contains CNN logic circuits operatively coupling to a memory subsystem, which includes a first one-time-programming (OTP) memory for filter coefficients and a second memory for imagery data. A second CNN processing unit contains CNN logic circuits operatively coupling to a memory subsystem that includes a first memory for filter coefficients, a second memory for imagery data and a third OTP memory for unique data pattern (e.g., security purpose). Either STT-RAM or OST-MRAM can be configured as different memories of the memory subsystem.

Buffer memory architecture for a CNN based processing unit and creation methods thereof

Embedded memory subsystems in a digital integrated circuit for artificial intelligence are disclosed. A semi-conductor substrate contains CNN processing units. Each CNN processing unit includes CNN logic circuits and an embedded memory subsystem. The memory subsystem includes first embedded memory and second embedded memory. The first embedded memory contains an array of MTJ STT-RAM cells with each cell has a circular planar area with a diameter in a range of 40-120 nm. The second embedded memory contains an array of MTJ STT-RAM cells with each cell has a circular planar area having a diameter in a range of 30-75 nm.

Embedded memory subsystems for a CNN based processing unit and methods of making

CNN (Cellular Neural Networks or Cellular Nonlinear Networks) based digital Integrated Circuit for artificial intelligence contains multiple CNN processing units. Each CNN processing unit contains CNN logic circuits operatively coupling to a memory subsystem having first and second memories. The first memory contains magnetic random access memory (MRAM) cells for storing weights (e.g., filter coefficients) while the second memory is for storing input signals (e.g., imagery data). The first memory may store one-time-programming weights or filter coefficients. The memory subsystem may contain a third memory that contains MRAM cells for storing one-time-programming data for security purpose. The second memory contains MRAM cells or static random access memory cells. Each MRAM cell contains a voltage-controlled magnetic anisotropy (VCMA) based magnetic tunnel junction (MTJ) element. Magnetization direction in VCMA based MTJ element can be in-plane or out-of-plane.

CNN based digital IC for AI contains a number of CNN processing units. Each CNN processing unit contains CNN logic circuits operatively coupling to a memory subsystem. A first subsystem includes an array of first magnetic random access memory (RAM) cells for storing weights and an array of second magnetic RAM cells for storing input signals. A second subsystem includes an array of first magnetic RAM cells for storing one-time-programming weights and an array of second magnetic RAM cells for storing input signals. A third subsystem includes an array of first magnetic RAM cells for storing weights, an array of second magnetic RAM cells for storing input signals and an array of third magnetic RAM cells for storing one-time-programming unique data pattern for security identification. Either MLC STT-RAM or MLC OST-MRAM containing at least two MTJ elements can be configured as different memories for forming memory subsystem.

MLC based magnetic random access memory used in CNN based digital IC for AI

Embedded memory subsystems in a digital integrated circuit for artificial intelligence are disclosed. A semi-conductor substrate contains CNN processing units. Each CNN processing unit includes CNN logic circuits and an embedded memory subsystem. The memory subsystem includes first memory and second memory. The first memory contains an array of MTJ STT-RAM cells with each cell has a circular planar area with a diameter in a range of 40-120 nm. The second memory contains an array of MTJ STT-RAM cells with each cell has a circular planar area having a diameter in a range of 30-75 nm.

Fabrication methods of forming memory subsystem of CNN based digital IC for AI are disclosed. The method in SLC technology includes: providing a metal layer, forming a via layer, forming a HSL, forming a MTJ element layer and then etching out unmasked portions of the MTJ element layer to form at least two groups of different sized MTJ elements. The method in MLC technology includes: providing a metal layer, forming a via layer, forming a first HSL, forming a first MTJ element layer, etching out unmasked portions of the first MTJ element layer to form lower MTJ elements, forming a second HSL, forming a second MTJ element layer and etching out unmasked portions of the second MTJ element layer to form upper MTJ elements. Same sized first MTJ element layer and the second HSL are formed together.

Fabrication methods of memory subsystem used in CNN based digital IC for AI

Systems and methods for forming embedded memory in a processing unit. The methods include: depositing a dielectric layer on a metal landing pad of a logic circuit of a processing unit; opening vias in the dielectric layer; filling in the vias; performing chemical mechanical polishing (CMP); depositing an adhesion and topography planarization (ATP) layer; etching away portions of the ATP layer; filling in with inter layer dielectric (ILD) materials; performing CMP; depositing a MTJ film layer; patterning and etching away portions of the MTJ film layer; filling in with dielectric materials; performing CMP; and forming a bit line on the top layer. The methods may also include annealing in a forming gas during different steps of the above processed to reduce the high stress from the making of multi-metal layers of the processing unit at high temperature. This may prevent wafer warpage and/or significant topography in the fabrication process.

Process of fabricating embedded spin transfer torque memory for cellular neural network based processing unit

A method of forming a sensor array comprising a series connection of parallel GMR sensor stripes that provides a sensitive mechanism for detecting the presence of magnetized particles bonded to biological molecules that are affixed to a substrate. The adverse effect of hysteresis on the maintenance of a stable bias point for the magnetic moment of the sensor free layer is eliminated by a combination of biasing the sensor along its longitudinal direction rather than the usual transverse direction and by using the overcoat stress and magnetostriction of magnetic layers to create a compensatory transverse magnetic anisotropy. By making the spaces between the stripes narrower than the dimension of the magnetized particle and by making the width of the stripes equal to the dimension of the particle, the sensitivity of the sensor array is enhanced.

GMR biosensor with enhanced sensitivity

Growing community of inventors

Pleasanton, CA, United States of America

Chyu-Jiuh Torng