Freescale Semiconductor,inc.

International Business Machines Corporation

Motorola Corporation

Globalfoundries Inc.

Advanced Micro Devices Corporation

Globalfoundries U.S. 2 LLC

Ramachandran Muralidhar

Rajesh A Rao

Leo Mathew

Bruce E White

Robert F Steimle

Tushar Praful Merchant

Theodore Gerard Van Kessel

Ghavam G Shahidi

Hendrik F Hamann

Sufi Zafar

Michael Alan Sadd

Takashi Ando

Josephine B Chang

Isaac Lauer

Gowrishankar L Chindalore

Viorel C Ontalus

Robert H Dennard

Siyuan Lu

Tak H Ning

Kevin K Chan

Robert Dennis Miller

Paul Michael Solomon

Dirk Pfeiffer

Dennis Merton Newns

Philip Joseph Oldiges

Craig T Swift

Sucharita Madhukar

Devendra K Sadana

Chung H Lam

Daniel C Edelstein

Vijay Narayanan

Daniel Joseph Friedman

Bich-Yen Nguyen

Stephen McConnell Gates

Eduard Albert Cartier

Unoh Kwon

Frank Robert Libsch

Jin Cai

Robert L Wisnieff

Richard Harold Boivie

James David Burnett

Seiji Munetoh

David L O'Meara

Jane A Yater

Charanjit Singh Jutla

Kohji Hosokawa

Robert J Baseman

Wanki Kim

Erwin J Prinz

Chandrasekara Kothandaraman

Nam H Nguyen

Bo Jiang

Bruce Bennett Doris

Dureseti Chidambarrao

Jeffrey Bowman Johnson

Amlan Majumdar

Martin Michael Frank

Min Yang

Barry P Linder

Dae-Gyu Park

Chitra K Subramanian

Xinhui Wang

Jin-Ping Han

Jun Song Wang

Jin Ping Han

Dung Tien Phan

Young-Hee Kim

Dharmesh Jawarani

Tien Ying Luo

Kai Xiu

Dechao Guo

Timothy Joseph Dalton

Praneet Adusumilli

Marwan H Khater

Levente Klein

Satyanarayana Venkata Nitta

Siddarth A Krishnan

Kailash Gopalakrishnan

Marius K Orlowski

Chun-Li Liu

Edward William Kiewra

Jianshi Tang

Sung-Taeg Kang

Brian A Winstead

Srikanth Balaji Samavedam

Ko-Min Chang

Tab Allen Stephens

Michael Alan Schappert

Robert J Miller

Konstantin V Loiko

Paul A Ingersoll

Matthew Thomas Herrick

Ildar Khabibrakhmanov

Dzung Tien Phan

Wayne M Paulson

Fateh A Tipu

Ashish V Jagtiani

Matthew W Stoker

Youngdeok Hwang

Veeraraghavan Dhandapani

A method for process control using predictive long short term memory includes obtaining historical post-process measurements taken on prior products of the manufacturing process; obtaining historical in-process measurements taken on prior workpieces during the manufacturing process; training a neural network to predict each of the historical post-process measurements, in response to the corresponding historical in-process measurements and preceding historical post-process measurements; obtaining present in-process measurements on a present workpiece during the manufacturing process; predicting a future post-process measurement for the present workpiece, by providing the present in-process measurements and the historical post-process measurements as inputs to the neural network; and adjusting at least one controllable variable of the manufacturing process in response to the prediction of the future post-process measurement.

Tool control using multistage LSTM for predicting on-wafer measurements

A method of improving at least one of quality and yield of a physical process comprises: obtaining values, from respective performances of the physical process, for a plurality of variables associated with the physical process; determining at least one Gaussian mixture model (GMM) representing the values for the variables for the performances of the physical process; based at least in part on the at least one GMM, computing at least one anomaly score for at least one of the variables for at least one of the performances of the physical process; based on the at least one anomaly score, identifying the at least one of the performances of the physical process as an outlier; and, based at least in part on the outlier identification, modifying the at least one of the variables for one or more subsequent performances of the physical process.

Model management for non-stationary systems

A sensor including a surface plasmon resonance detector with a reservoir for containing a liquid sample. The sensor further includes a sensing metallic film positioned within the reservoir so that at least a majority of a surface of the sensing metallic film is to be in contact with the liquid sample being housed within the reservoir. The sensory also includes a semiconductor device having a contact in electrical communication with the sensing metal containing film that is positioned within the reservoir. The semiconductor device measures the net charges of molecules within the liquid sample within a Debye length from the sensing metallic film.

Biosensors including surface resonance spectroscopy and semiconductor devices

Anomaly detection and remedial recommendation techniques for improving the quality and yield of microelectronic products are provided. In one aspect, a method for quality and yield improvement via anomaly detection includes: collecting time series sensor data during individual steps of a semiconductor manufacturing process; calculating anomaly scores for each of the individual steps using a predictive model; and implementing changes to the semiconductor manufacturing process based on the anomaly scores. A system for quality and yield improvement via anomaly detection is also provided.

Anomaly detection and remedial recommendation

A private key of a public-private key pair with a corresponding identity is written to an integrated circuit including a processor, a non-volatile memory, and a cryptographic engine coupled to the processor and the non-volatile memory. The private key is written to the non-volatile memory. The integrated circuit is implemented in complementary metal-oxide semiconductor 14 nm or smaller technology. The integrated circuit is permanently modified, subsequent to the writing, such that further writing to the non-volatile memory is disabled and such that the private key can be read only by the cryptographic engine and not off-chip. Corresponding integrated circuits and wafers are also disclosed.

Encryption engine with an undetectable/tamper-proof private key in late node CMOS technology

A method for measuring pollution that includes providing a plurality of analyte sensors arranged in a grid over a sensing area, wherein the analyte sensors measure a pollutant, and positioning at least one current sensor in the sensing area. A pollution source is localized using a pollution source locator including a dispersion model and at least one hardware processor to interpolate a location of a pollution source from variations in current measured from the current sensors and measurements of pollutants from the analyte sensors.

Leveraging air/water current variability for sensor network verification and source localization

Cross bar arrays and a method for forming cross-bar arrays are provided. The cross bar array device includes first conductive lines spaced apart and extending in a first direction in a first plane, the first conductive lines including a bottom electrode layer. Second conductive lines are spaced apart and arranged transversely to the first conductive lines in a second plane, the second conductive lines including a top electrode layer. An oxide layer formed on the bottom electrode layer of the first conductive lines and in contact with the top electrode layer of the second conductive lines such that a resistive element is formed through the oxide layer at intersection points between the first conductive lines and the second conductive lines. A multivalent oxide spacer that switches between at least two oxidative states on at least one sidewall of the oxide layer between the first plane and the second plane.

Multivalent oxide spacers for analog switching resistive memory

A method is presented for incorporating a metal-ferroelectric-metal (MFM) structure in a cross-bar array in back end of the line (BEOL) processing. The method includes forming a first electrode, forming a ferroelectric layer in direct contact with the first electrode, forming a second electrode in direct contact with the ferroelectric layer, such that the first electrode and the ferroelectric layer are perpendicular to the second electrode to form the cross-bar array, and biasing the second electrode to adjust domain wall movement within the ferroelectric layer.

BEOL cross-bar array ferroelectric synapse units for domain wall movement

A method of fabricating a symmetric element of a resistive processing unit (RPU) includes forming a substrate with a channel region connecting two doped regions, and forming a source above one of the two doped regions and a drain above the other of the two doped regions. A gate is formed above the channel region, and a bar ferroelectric is disposed above the channel region and below the gate.

Mobile ferroelectric single domain wall implementation of a symmetric resistive processing unit

Embodiments of the invention are directed to an integrated sensing system that includes a movable orientation device configured to dynamically position the movable orientation device based on receiving an air-flow. A gas sensor is coupled to the movable orientation device. The gas sensor includes a recognition element configured to detect a chemical in a plume. The movable orientation device is configured to perform a synchronized sensing operation that includes, based at least in part on the movable orientation device receiving the air-flow, moving the movable orientation device to dynamically maintain a predetermined orientation of the movable orientation device relative to a direction of the air-flow. The predetermined orientation includes positioning the gas sensor in a path of the air-flow, wherein the air-flow is influencing the plume to move along the path such that the recognition element is exposed to the plume.

Plume characterization using synchronized measurements of gas composition, wind direction, and wind speed

Encryption engine with an undetectable/tamper proof private key in late node CMOS technology

A method is presented for enabling heat dissipation in resistive random access memory (RRAM) devices. The method includes forming a plurality of conductive lines within an interlayer dielectric (ILD) and forming a RRAM stack over a conductive line of the plurality of conductive lines, the RRAM stack including a bottom electrode, a conductive pillar, thermal conducting layers, and a top electrode. The thermal conducting layers are disposed on opposed ends of the conductive pillar. The thermal conducting layers directly contact the top electrode and the bottom electrode. The thermal conducting layers include aluminum oxide (AlO).

Resistive RAM cell structure for gradual set programming

Heuristic-based techniques for gas leak source identification are provided. In one aspect, a method for identifying a location of a gas leak source includes: obtaining gas sensor data and wind data synchronously from a gas leak detection system having a network of interconnected motes comprising gas sensors and wind sensors, with the gas sensors arranged around possible gas leak sources in a given area of interest; identifying the location of the gas leak source using the gas sensor data and wind data; and determining a magnitude of gas leak from the gas leak source using the location of the gas leak source and a distance d between the location of the gas leak source and a select one of the gas sensors from which the gas sensor data was obtained. A gas leak detection system is also provided.

Heuristic based analytics for gas leak source identification

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Mahopac, NY, United States of America

Ramachandran Muralidhar