mpw-002 / INDM Inductor-TLM

the INDM shapes of all of mpw-002

40 of 40 dies rendered.

16,524,862 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (48.0 MB in total, largest 4.1 MB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-002_INDM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-002_INDM_reticle.png
the stitched reticle, full resolution
33.6 MB 44b6aa5b904b860b307d95e930e0ffae4242e5b802f841a365a6d1274b1ac577395e6d5e 5e70f329f9b712039b8db2e810bec7dfc1bb6894e81dd56d89d937968a973e15
mpw-002_INDM_reticle_overview.png
the same image scaled to fit 4096 px
2.5 MB ac85a7db2f4e65417bd729134ce5ffc414b1dfb3ec9d7731bee58d055ed208570fb488f1 555aa29ee7178d497df2a8ba350c147388ec989fc49a63da5589a8205e6f9c88
mpw-002_INDM_tiles.json
where every tile belongs, and its checksum
13.3 kB b226aa94024acc4901080ffd98a5dacf509c7e710ed84060233187b5c2af059135952505 96ff7ef6828c0164d2a310bfee65bcbcc570bb2f2273eaf883940e6abac74f18
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 axi_dma_using_spinal 7576×107760, 0 828.5 kB90850a78b851f3d25e4c314f9e770a3b6126b3b4b37820e113f3fb47a7805ea7
A2 wishbone_can 7576×107767576, 0 1.1 MB7c6decd6a8cc34ee5d32e72f103574f86f2752ac9f2d62f066b53dd29f015ad9
A3 Analog-Neural-Network 7576×1077615152, 0 669.7 kB5a0fecfc261be56940e4fba8821b4793595963c5b38a2b0c97fc3c668e604a4b
A4 caravel_dsp2 7576×1077622728, 0 795.8 kB1a24fb45f893a0190f74adc13c1be2d5b9c7947a93c86c29b01feed0951e2c4f
A5 eFPGA_v3_caravel 7576×1077630304, 0 4.1 MB14fa9519d166e6011883401aabe0f805f5d4ce2fe44c5629982ae3c2a1c39cc9
A6 opencryo_testchip 7576×1077637880, 0 669.3 kBf6be2cf103c09b61e3a9eed3a579e864905926c129fac243fd90303ccbea839c
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 905.9 kBa907ef7cf2b54ae95df12185489727dd93cc3e29ea286bf1dfd3d5485f9564a7
B2 lexicon 7576×107767576, 10776 1.6 MB5a2fe4915f80a004e4c70d08e898757fadeb9b68a26feb7534fd8ca7d6723c41
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 1.1 MB57b2f240922320fe9964bef7b4f02a8c600355ad8199d38d990668dbb60e84ff
B4 yonga-lz4_decoder 7576×1077622728, 10776 830.1 kBddacf7567d445f9f8af3f1bb70ce2f6addc87ccf0c80addd0e96e9d9f5472317
B5 Potentiometric_Digit 7576×1077630304, 10776 677.2 kBfe67dc6499a027a0d9cf54136adf971f8708be43197326f239fcc91e38091d31
B6 fuserisc_ver2 7576×1077637880, 10776 3.8 MB072456093d5a50bd88a20a5b2ae59fa1eca7ea65ee20750da64cb98e34685419
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 744.7 kBd716ef374f8f160049e60a6aede24104e678da4449547504c3561507e3cdd14c
C1 Comparator_3v3 7576×107760, 21552 673.6 kBda62cce3d0787e0bae0b26c9de82497044eb43d01156bbb4a63c24485c50ac22
C2 caravel_multi_encode 7576×107767576, 21552 810.5 kB137a53523401c5b4ff264c404a082f21fa441792f8203501736ae2d5518ec3b2
C3 kasirgalabs_uart 7576×1077615152, 21552 786.8 kBce4c648666ee5c00a7e1b82f4f0f92bb7d68c3aba1a24fbd629072c8d3589130
C4 Automatically_genera 7576×1077622728, 21552 777.0 kBf562631f8d781ba19d2a9f15895b1867798adacdcdff2e4182ed8358eae417f6
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 1.9 MB0d22847b5b0f1842dd59d8e5217fbf2253b758c0d59d853238c1253d3f3f3f74
C7 caravel_azadi_soc 7976×1077645456, 21552 966.9 kBcccb32c426cf49fc67a7f624219510c080b6d0cecfaad0c975901405858e9a0b
D1 caravel_dsc 7576×107760, 32328 799.7 kB3d20a122d6877b54159db11926d401394b6778dd707b0f2c5e273bbf199635e1
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 2.2 MB1f407a99e6733b10bfa11f409676d861c14566b3b40536ddda756fd1cebf9c27
D3 caravel_user_project 7576×1077615152, 32328 786.8 kB25c5073d9bfa6b2e1f68bf7aeabc80d89a2e2f2e4c1cf3f32f9e02ac13f8246a
D4 General_Purpose_Band 7576×1077622728, 32328 773.4 kB49adfc4398cabc0fa22fb37a3c5bd882b5f84b95e91df35c3b831fc3681b29bb
D5 zero_to_asic 7576×1077630304, 32328 1.1 MBe7c31d450257b2ba2971f7ffcb8885f65bf583bdcbf59d26e3891837b8671a39
D6 fast_gcd_for_large_i 7576×1077637880, 32328 3.9 MB31618e4757c85bd10f4171ffd1da732fe924dbab391c28051cbb2dd9f6982a26
D7 Columbus 7976×1077645456, 32328 676.8 kBfbc31f1cff29c40d5377d0af1dfd62fd69424d1467bef68fa8c5c48921cb9a96
E1 Space_shuttle 7576×107760, 43104 1.2 MB5cb839ea8eb8133c06f8823736232d49773f5325411e66d1abfcb38cb342e4d1
E2 subservient 7576×107767576, 43104 921.6 kB836a20fb6c2b326e2553b1561a68db7bf7f027d14e019adfff56464a7d304c27
E3 caravel_analog_fulgor 7576×1077615152, 43104 678.7 kB514b3736d85609adbb69caaf6b8f6c2ab54600486c2aec0224125e988e06ee69
E4 bandgap_reference_de 7576×1077622728, 43104 773.4 kB02ab2b299fd6fb4d9ba5d812ff58f414add82b664ab27d41a1ace30eaf408f3e
E5 renml 7576×1077630304, 43104 1.2 MB495bb56145e16f956e90415b1595fc9279bf86c4e33c9b1b90f9fb143bea2e88
E6 io_expander 7576×1077637880, 43104 783.9 kBd5fe2c3914c23b9ab0132a8e0c1fcf28de1ce3146661a943d180c0504533a18f
E7 digital_pll 7976×1077645456, 43104 695.1 kB49933d597b76587bcba53d6adacc0453badeb41552c430dfa5317b5b79e0afe6
F1 zero_to_asic 7576×111760, 53880 1.2 MB2e913676068b948bcff0e1a3974c0b653c53d95104b1f04c7c14ccaa113a3c78
F2 caravel_fpu 7576×111767576, 53880 816.8 kB46465aca04886eb0615ddd1dd77d61359970df49a2656ddf6783177e3ba89283
F3 BrqRV_EB1 7576×1117615152, 53880 2.2 MBb879027d4ff9b5cef46820bf0cb87f058b2d8842426fe66dd275aa783ac71ea2
F4 hilas 7576×1117622728, 53880 675.1 kBe72344b546abb8cfa48af9d62c607099848ee5700a04999766921a6a54f96476
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 673.4 kB593bf9b80296c668dd9875686572788b173f06637a75330d9ddfff81a2918891
F6 sha1_engine 7576×1117637880, 53880 1.0 MB82ef727aa52cfb6c4aaa2cd4c368f96e5a640dbd9ddaf31e4f45bbf5ca929bc8
F7 caravan_openfasoc 7976×1117645456, 53880 1.1 MBc22014072be1ebd24b701ab32b00ed3fc4c4d0bd50e2b9d4367ded7048fcd274

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About INDM on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask INDM

Mask layer
INDM — Inductor-TLM
Drawn layer
70:20
Detail
INDM = met3 (thick-last-metal flow, not SKY130)
Used in SKY130
no

mpw-002 — SKY130 MPW-2

Foundry product
EF MPW#2
Reticle set
5CS8007AC
Run date
2021-06-18
Submissions closed
2021-06-18
Projects
56
Fab lot
4205819
Identification
confirmed — Lot records: EF MPW#2, lot 4205819 = 'SKY130 MPW 2'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-2 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-002/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.