Feed-forward run-to-run control for reduced parametric transistor variation in CMOS logic 0.13&amp;#x03BC;m technology

Jedidi,; Sallagoity,; Roussy,; Dauzere-Peres,; Pinaton,

doi:10.1109/issm.2007.4446831

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…The technique is based on the start/static sampling technique but the main difference is that the number of lots or wafers to select is adjusted throughout production depending on the process state. Most feedback and feed-forward process control techniques are used in conjunction with Statistical Process Control (SPC) techniques to improve sampling efficiency and effectiveness [30] [31]. Table III presents a survey of adaptive sampling techniques in semiconductor manufacturing.…”

Section: Adaptive Samplingmentioning

confidence: 99%

A Literature Review on Sampling Techniques in Semiconductor Manufacturing

Nduhura-Munga

Rodriguez-Verjan

Dauzère‐Pérès

et al. 2013

IEEE Trans. Semicond. Manufact.

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This paper reviews sampling techniques for inspection in semiconductor manufacturing. We discuss the strengths and weaknesses of techniques developed in the last last 20 years for excursion monitoring (when a process or machine falls out of specifications) and control. Sampling techniques are classified into three main groups: static, adaptive, and dynamic. For each group, a classification is performed per year, approach, and industrial deployment. A comparison between the groups indicates a complementarity strongly linked to the semiconductor environment. Benefits and drawbacks of each group are discussed, showing significant improvements from static to dynamic through adaptive sampling techniques. Dynamic sampling seems to be more appropriate for modern semiconductor plants.

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Section: Adaptive Samplingmentioning

confidence: 99%

A Literature Review on Sampling Techniques in Semiconductor Manufacturing

Nduhura-Munga

Rodriguez-Verjan

Dauzère‐Pérès

et al. 2013

IEEE Trans. Semicond. Manufact.

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“…In particular, the control of transistor threshold voltage (Vth) variation has become significant to preventing parametric yield loss in an advanced SoC [1], [2]. A practical application that incorporates equipment data into a process control has been significant to solving these problems.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Control of Transistor Threshold Voltage by Virtual Metrology (Virtual PCM) Using Equipment Data

Tanaka

Yasuda

2013

IEEE Trans. Semicond. Manufact.

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This paper is a description of how to predict and control the transistor threshold voltage (Vth) for an advanced system on chip (SoC) by virtual metrology (VM), which we call virtual process control module (PCM), by using equipment data. Impact analysis for Vth variation by using a Virtual PCM model indicates that the impact of source and drain (S/D) resistance and extension resistance are comparable with that of the shape factor (i.e., gate length, sidewall width). Virtual metrology models for the resistance were developed to control and predict Vth. As a result of the VM control, Vth variation was reduced by 28%. Moreover, in-process prediction of Vth was put into practical use.Index Terms-Control, equipment data, prediction, system on chip (SoC), transistor threshold voltage (Vth), virtual metrology (VM).

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Feed-forward run-to-run control for reduced parametric transistor variation in CMOS logic 0.13μm technology

Cited by 2 publications

References 9 publications

A Literature Review on Sampling Techniques in Semiconductor Manufacturing

A Literature Review on Sampling Techniques in Semiconductor Manufacturing

Prediction and Control of Transistor Threshold Voltage by Virtual Metrology (Virtual PCM) Using Equipment Data

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