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Symbolic analysis techniques offer a complementary way to analyze analog
integrated circuits. Since the results of symbolic analysis are analytic equations that
explicitly describe the functionality (e.g. transfer function, impedance, pole/zero...) of
the circuit as a symbolic expression of the design variables, it lends itself more for
designers to gain insight into the behavior of the circuit. Questions like “how does the
circuit actually work?”, “at what node is the dominant pole located?”, “what is the
relation between the bias current and the gain-bandwidth?”, “what is the impact of
device mismatch on the PSRR of this circuit”, “which transistor(s) actually is the
dominant noise source?”, “which nonlinearity is creating the most third-order
distortion?” etc. can be answered by means of symbolic analysis. Originally
hampered by the exponential complexity of the symbolic analysis problem,
approximation techniques that typically exploit designer intent or common design
practice had to be developed to make the results interpretable for humans. Symbolic
expressions are however also useful in many other applications that require the
repeated evaluation of circuit characteristics for a wide range of parameter values,
such as for example encountered in circuit optimization or in yield analysis. Not
requiring interpretation by humans, a wide range of efficient algorithmic techniques
iv
has been developed in the past years to overcome the complexity issue and to make
symbolic analysis tractable for circuits of practical size.
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eetop公众号
创芯大讲堂
创芯人才网
发表于 2016-5-30 12:18:22
发表于 2016-5-30 19:42:28
